Your Language Model Secretly Contains Personality Subnetworks
Authors: Ruimeng Ye
Zihan Wang
Zinan Ling
Yang Xiao
Manling Li
Xiaolong Ma
Bo Hui
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Persona Modeling
Primary area: foundation or frontier models, including LLMs

Abstract: Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning.
We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters?
In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetworks from the model that lead to binary-opposing personas, such as introvert-extrovert? 
To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space—pointing toward a new perspective on controllable and interpretable personalization in large language models. Our code is available at https://github.com/Ruimeng-Ye/Persona.git.

Published as a conference paper at ICLR 2026
YOUR LANGUAGE MODEL SECRETLY CONTAINS PER- SONALITY SUBNETWORKS
Ruimeng Ye', Zihan Wang', Zinan Ling', Yang Xiao Manling Li, Xiaolong Ma', Bo Hui! Universitt of Tulsa Worthwestern University *University of Arizona (ruy9945, bo-hui (utuiieeuussa..... edu
ABSTRACT
Humans shift between different personas depending on social context. Large Lan- guage Models (LLMs) demonstrate similar flexibility in adopting different per- sonas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such prompting, retrieval- -aggeeeted generation (RAG), or fine-tuning. We ask: do LLMS really need external context or pa- rameters adapt different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialize subnetworks in their parameter space. Using small calibra- tion datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetworks from the model that lead to binary- opposing personas, such as introvert-extroverr! To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method entirely training- faining-free free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded their parameter space-pointie toward new perspective on con- trollable and interpretable personalization in large language models. Our code is available https:://// /ttp://ggtttbbbt
Ye/Persona.
INTRODUCTION
Humans shift between different personas depending context. A child may speak politely with a teacher yet joke casually with friends; the same adult might appear cautious and formal in job interview but warm and humorous family dinner. These shifts in tone, style, and behavior not learned separately for each context-they emerge naturally as flexible reconfigurations of the same underlying cognitive system.
Large language models (LLMs) are also capable of adopting different personas. They can gen- erate outputs that mimic diverse behavioral styles with carefully designed prompts, retrieval, or fine-tuning. However, a major division of current methods treats persona control as what must be externally imposed on a monolithic model: they involve training an ensemble of expert models, with each model specialized for single persona. Alternatives such retrieval-ane autrentelaugmmented generation (RAG) prompting reduce overhead, while often suffering from interference, shallow control, or unstable persona fidelity (as shown in Table This raises fundamental question: do LLMs really require external intervention to display different personas, or are these behaviors already embedded within their internal structure, waiting be uncovered?
Recent work has revealed that behavioral traits and capabilities LLMs are often encoded as in- terpretable directions in activation space (Cao et al. 2024| Chen al. 2025). This line of research demonstrates that model behaviors can be understood and potentially controlled through their inter- nal representations. Building these insights, investigate whether distinct personas naturally

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Published as a conference paper at ICLR 2026
Method
Training-
No extra Parameters
Time Cost
Prompt-basee 2023)
CCeng et al. 2023 Shao et al.
Instant
RAG-baset based
Zerhoudi Granitzer
2024
Instant
et al. 2024
Fine-tuning-basae
Zhou al.
2023 Wang
Hours days
2025
Ours (Pruning-based)
Minutes
Table I:
Comparison of persona modeling approaches. *Our method only requires lightweight binary masks, without introducing trainable parameters like LoRA or adapters.
emerge as separable sub-structures within a single pretrained model. Our key observation is that when presented with persona-specific inputs, different neurons exhibit consistently distinct activa- tion patterns, suggesting that personas may already exist as latent, disentangled pathways in the network.
Inspired by this finding and recent advances in neural network pruning (Sun et al. 2023 Frantar & Alistarh 2023). we propose a novel perspective: persona capabilities can be extracted as sparse subnetworks from a single pretrained LLM without any additional training. Rather than viewing personas as behaviors be learned through fine-tuning, we treat them as pre-existing eircuits to be discovered and isolated through structured pruning. This approach is motivated by the lottery ticket hypothesis (Frankle & Carbin 2019), which demonstrates that sparse subnetworks can match the performance of dense models. We extend this principle to show that multiple "winning tickets" corresponding to different personas coexist within single pretrained model.
In this paper, present train-free framework for extracting persona- -peesaaapeeeillizedd subnetworks through activation-guii -uuided pruning. Our method requires only small calibration datasets to identify and isolate persona-relevant parameters. Furthermore, we introduce contrastive pruning strategy specifically designed for scenarios where personas form natural oppositions, ensuring that the ex- tracted subnetworks are maximally disentangled.
Our contributions are threefold:
We demonstrate that distinct personas manifest as separable activation patterns pre- trained LLMs, and these patterns can guide the extraction of specialized subnetworks with- out any gradient updates.
We propose a contrastive pruning algorithm that explicitly maximizes parameter disentan- glement between opposing personas, achieving stronger behavioral separation than stan- dard pruning methods.
We conduct extensive experiments across diverse persona evaluation settings, showing that our extracted subnetworks achieve superior persona alignment compared prompting and other baselines, while maintaining fluency and reducing inference costs through sparsity.
Our work challenges the conventional paradigm of training separate models or adapters for differ- ent personas. Instead, we show that single pretrained model already contains the capacity for diverse personas. which can be efficiently "unmixed" through principled pruning. We argue that these behaviors are not externally induced but embedded as sparse routing structures in parameter space, which can be systematically uncovered through pruning, enabling efficient and training-fres persona switching.
2 RELATED WORK
Persona Modeling in LLMs Recent work has explored various approaches to imbue LLMs with distinct personas and personalities, including fine- mee-tuning on curated character datasets, role-playing evaluations via character interviews. and prompt-based induction of personality traits (Shao et al. 2023 Wang et al., 2023 Scrapio-Garee et al.) 2023). Building on this, advances in personaliza- tion and preference following long-context evaluation, inference time control, embedding strategies,
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and progressive adaptation frameworks (Zhao al. 2025 He al. 2025 Liu al. 2025 Mok
et al. 2025| Zhang et al. 2025). Representative approaches include memory augmented prompting
((Madaan et al. 2023)), retrieval and profile-based methods (Mysore al. 2023 Zhuang et al. 2024)), parameter efficient adaptation ((Zhu et al.) 2024; Zhang et al. 2024)), and a alignment-based optimization ([Zhou al.] 2024)). However, these approaches often rely costly fine-tuning, retrieval-augmented pipelines, or prompt based heuristics that lack robust control, while parameter- efficient adaptations still require additional training. Our work differs by extracting persona capa- bilities directly from pretrained models without additional training.
Network Pruning and Sparse subnetworks The lottery ticket hypothesis (Frankle Carbin 2019 Liu et al. 2024a Hui et al., [023 Tiang et al.) (023) demonstrates that dense networks contain sparse subnetworks capable of matching full model performance. Recent advances have extended pruning to LLMs: Wanda (Sun et al., (203)) prunes weights based magnitude and ac- tivation patterns, while SparseGPT (Frantar & Alistarh 2023) uses second-order information for more accurate pruning. (Ma al., 2023) propose structured pruning for task-agnostic compression. Unlike these works that focus on general compression, we leverage pruning to discover and isolate persona-specific sub-circuits within single model.
Mechanistic Interpretability and Model Circuits Understanding internal representations has re- vealed that specific behaviors correspond to interpretable activation patterns (Elhage et 2022 Olsson et al. 2022). (Li et al. 2023) identify "truth directions" in activation space that control factuality. (Zou al. 2022) demonstrates that concepts and behaviors can be manipulated through activation steering. (Geva et al., 2023) shows that feed-forward networks act as key-value memories encoding factual knowledge. Our approach builds on these insights by showing that personas mani- fest as distinct activation patterns that can guide structural decomposition. Unlike activation steering or linear representation editing, which operate directly in hidden state space at runtime, our method uncovers sparse routing structures in parameter space. This distinction enables zero-shot persona switching without injecting additional activation vectors or editing weights.
METHODS
Figure Figure[provideee provides an overview our framework for persona specialization: given pretrained LLM and small persona-specific calibration data, we first collect activation statistics, then construct binary masks that isolate persona-relevant subnetworks, and finally apply these masks at inference to obtain controllable persona behavior. Implementation details provided in Appendix K
3.1 PROBLEM SETUP
For each persona E P, we assume access to a small calibration dataset D, - {xx sf H su represents input prompts and vf represents persona-consistent responses. These calibration sets are orders of magnitude smaller than typical training datasets, usually containing only hundreds to a few thousand examples per persona. Our objective find masks that maximize persona alignment while maintaining sparsity:
where
max E, [log PM, (ylx))
M
where ||MP|o < (1 p)d enforces a sparsity constraint on the mask, and p E (0, 1) is the target sparsity ratio. We treat each Linear weight matrix W E Rmxn, where columns correspond to input channels. Unless stated otherwise we prune all Linear modules in attention and MLP blocks; embeddings and the LM head not pruned. The sparsity ratio p is applied per layer and, unless otherwise specified, is shared across all layers. We denote each weight matrix by W = [wel] E Rmxa with i indexing output channels and j indexing input channels. Each persona-specific mask MP.(1) E {0, 1) jmx" is defined per layer, and the global mask M is formed by concatenating them across modules, which yields the persona-speciallzee model Mp = ʃ(0 @ MP).
3.2 PERSONA SUBNETWORKS VIA PRUNING
Our approach leverages the key observation that sprsona- inputs induce distinct neuron ac- tivation patterns. For given layer 1, with input activations (9) (3)))) E IR under input x, we collect
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INTJ
ENFP
ESTP
ISTJ
Persona Data
Pruning
Sub-networks
The figure illustrates our pruning framework. Persona specific data is employed to com- pute activation statistics and importance scores, which are then used to rank parameters and struct masks that isolate persona-relevant subnetworks. Colored entries mark the Top-K parameters retained for each output neuron, while gray entries pruned.
Figure 1:
activation statistics across the calibration data:
A [ [j] E(,Y)),, [1 [x)]
which captures the expected activation magnitude for neuron j under persona p's data distribution. Magnitude-based Importance Scoring For linear layer with weights W IR mxx,, compute importance scores that combine weight magnitude with activation frequency:
S
[ A [}].
The intuition is that parameters both large in magnitude and frequently activated by persona-speciff inputs are most critical for that persona's 's behavior. We perform row-wise Top-K: for each output channel i, we keep K = [(1 p) n1 input columns with the largest zj values and set the rest to zero, yielding binary mask M E {0, m
1 {1 S E TopK ((5))
M
(4)
otherwise,
Refinement To account for parameter interactions, we optionally employ refinement criterion inspired by second-order pruning methods. Specifically, we approximate parameter sensitivity using per-dimension input variance estimated from calibration data, with small damping constant À to ensure numerical stability. These variance- normalized scores are applied column-wise to rank input features, guiding the pruning order:
Σ (x) +
(5)
Dpl (x.g)~>p
where we retain the diagonal entries as an efficient approximation.
3.3 EFFICIENT INFERENCE VIA DYNAMIC MASKING
During inference, we apply persona-specific masks without modifying the original weights:
y (W MP)x b

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This enables rapid persona switching with minimal overhead. We optionally support soft gating mechanism
G= M2 + +1 MP), 7 [0,1),
where 7 0 corresponds to standard hard masking. This enables efficient persona switching without modifying model parameters or caches.
Contrastive Pruning Many personas naturally form semantic oppositions (e.g., power-seeking power-rejecting). Standard pruning methods may yield overlapping masks that fail to capture con- trasting behaviors, as they optimize importance scores independently for each persona. We therefore introduce a contrastive pruning strategy that explicitly maximizes parameter separation between op- posing personas (p- by leveraging differential activation patterns.
For opposing persona pairs, we collect activation statistics Paj M and variances d. J. across their respective calibration data. In the contrastive Wanda variant, importance is defined by scaling weight magnitudes with standardized activation differences:
S = |wayl .
(8)
Vo'j + tof +
where & monotonic activation function (e.g.. ReLU or softplus) and is a small constant for numerical stability. In the contrastive-Sppe -parsse---eseeeeeeee variant, we normalize importance scores column-wise and compute contrastive importance as:
SP sj
Cif = S 50-
5
Σx
ie
Parameters are then ranked according to their persona- scores and assigned to disjoint masks MP ,MP with each parameter allocated to the persona exhibiting the larger score, encourag- ing the distribution divergence of local neurons that could result in personality separation. This procedure can be interpreted as minimizing an overlap regularizer S2(MP+, MP-) alongside the cal- ibration loss. By explicitly modeling opposition rather than treating personas independently, both variants encourage divergent subnetworks that achieve superior specialization for contrasting behav- iors while maintaining computational efficiency through training- free mask construction.
Note that our local contrastive constraint that encourages the pruning procedure to make explicit trade-offs between competing activations, assigning each high- high-importance parameter the persona for which it is most informative. Crucially, this does not imply that the underlying activation distribu- tions non-overlapping that the entire resulting subnetwork structure is orthogonal, especially since shared components like the LM head remain unpruned. For example, two different personal- ity subnetworks have sparsity of 40%, there will be overlap between the subnetworks. We have analyzed the similarity and overlap between different personas in Section 3.4 and the Appendix G
3.4 MASK ANALYSIS
While the pruning method successfully elicits distinct personas Llama 2-13B, we observe a notable variance in success rates: some personas are clearly differenti- ated, while others remain entangled or fail emerge. To investigate the underlying mechanisms, we analyze the pruned sub- networks along two axes: (i) mask-level separation and (ii) layer-wise representation similarity. Table 2 provides the average
Dimension Pair
Avg.
Diff. (%)
Attn
MLP
I E N vs.
1.34
1.28
1.44
0.75
0.75
0.76
Fvs. T
1.08
1.03
1.14
vs. P
0.76
0.73
0.79
Table 2: Average differential mask ratios across MBTI dimensions.
differential ratios across MBTI dimensions, serving
measure of mask-level separation. We observe that I/E and E/T pairs exhibit substantially higher divergence than N/S and J/P, sug- gesting that certain dimensions Moreover, across all dimensions, differences are consistently larger in MLP blocks than in attention layers, indicating that persona separation primarily relies feed-forward transformations rather
strongly encoded in the model's internal representations.
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than routing effects. These findings explain the uneven of persona switching: dimensions with weak mask separation (c.g.. N/S, J/P) tend to collapse into adjacent types, while dimensions with stronger separation (VE,,,I/E, F/T) yield more robust and consistent persona behaviors.
Beyond average ratios, however, persona switching succeeds only when the pruned sub- network yields activation margins that clearly separate the target persona from its nearest neighbors at upper layers. Table 3 high- lights representative cosine similarities at mid- dle (L25) and upper (L39) layers, Strikingly, the first two rows (INFI-INFP and ISTJ-ESTJ) correspond to persona switches that fail in prac- tice, with margins remaining extremely small. By contrast, base to persona distances (rows 3-4) are much larger at L25 (Re 0.44) but con-
Persona Pair
Layer 25
Layer 39
INFI INFP
0.9688
0.9883
ISTJ ESTJ
0,9609
0,9727
INFI 0.4414
0.8320
Base INFP
0,4375
0.8320
Table Representative cosine similarities mid- dle (L25) vs. upper (L39).
tract z 0.83 at L39, reflecting increased overlap higher layers. These narrow inter- inter-persona gaps explain failures like INFP collapses and STJ/NTJ cross-flips despite visible I/E and F/T separations: the N/S and J/P axes do open sufficiently large margins the top of the network, so outputs gravitate toward the closest attractor in representation space. Practically, this suggests two levers: (i) dimension -waare aware sparsification that allocates higher sparsity to weakly separated axes (N/S, J/P), and (ii) layer-award masking that increases discrimination specifically in late MLP blocks where personas are most entangled. Together, these adjustments align with the single-dimension analysis (stronger I/E and F/T; weaker N/S and J/P) and account for why some personas switch cleanly while others collapse into adjacent types.
MBTI Dimension Scores with Wanda
MBTI Dimension Scores with Sparse
Base Model
Model
INF)
INF]
ISTP
ISFP
INFP
ESF)
ENPj
ENP)
Esr]
[S#)
ENTP
INTP
INT]
ESPP
15
ESTP
ENT]
(ST)
(ST]
1-E
S-N
T-f
P4
S-N
T-F
Figure 2: MBTI Heatmap.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets We evaluate our method on three persona-related datasets: (1) MBTI (Cui et al. which provides question-answer pairs for Myers- personality types. (2) Persona (Perez ct al., 2023). covering power-seeking, wealth-seeking, and hallueination-idenifiaaieee behaviors. (3) RoleAgentBench (Liu al. 2024b), role-playing dialogue benchmark. Our experiments are conducted on LLMMA-2-13B LLMMA-3-8B, and Qwen2.5-1444 as base models. We have investi- gated the difference between base models and instruction-tur models in terms of personalization. Additional implementation details can be found in the Appendix H and Appendix[]
2023
Baselines To verify the effectiveness of our proposed pruning framework, compare it against two representative train-free free baselines. 1) Prompt: persona instructions are injected directly into the input prompt. 2) Retrieval-Augmennte Generation (RAG): the model retrieves the top-k persona- relevant samples from a small reference set and concatenates them with the input. (3) Supervised Tine-Tuning (SFT).

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Method
Power-Seeking (%)
Wealth--Sekingg (%)
Hallucination ( (%)
Prompt (Llama-2-13B)
41.0
44.0
58.5
Rag (Llama-2-13B)
45.5
50.5
64.5
Wanda (lama-2--138
51.5
54.5
89.0
Sparse (Llama-22 --mna----11 2-13B)
52.0
58.5
84.5
Wanda with Contrastive
54.0
66.0
95.0
Pruning (Llama-2-13))
Sparse with Contrastive
56.5
64.5
96.0
Pruning (Llama-2-1a) 2-33))
SFT (Llama-2-138)
64.0
71.0
97.5
Prompt (Llama-3-8B)
45.5
47.5
63.5
Rag (Llama-3-8B)
52.5
59.0
72.5
Wanda (Llama-3-8B)
57.0
64.0
86.0
Sparse (Llama-3-8B)
59.5
65.5
87.5
Wanda with Contrastive
58.5
69.5
94.0
Pruning (Llama-3-8B)
Sparse with Contrastive
60.5
66.0
96.0
Pruning (Llama-3-8B)
SFT (Llama-3-8B)
69.5
71.5
98.5
Table 4: Comparison of pruning methods and baselines the Al Persona classification task, report-
ing accuracy.
Method
Friends (%)
Harry Potter (%)
Sherlock (%)
The Big
Merchant of Venice (%
Bang Theory (%)
Prompt (Jlama-2-13B)
18.37
42.06
42.11
29.55
41.67
Rag (Llama-2-13B)
23.47
45.24
44.74
34.09
45.83
Wanda (Llama-2 2---2-11-1 1-BB
39.80
48.41
52.63
52.94
50.00
Sparse (Llama-2-1 2-13B)
41.84
50.00
55.26
50.00
54.17
Prompt (Llama-3-8B)
18.37
42.06
42.11
29.55
41.67
Rag (Llama-3-88 3--B)
30.61
47.62
50.00
34.09
45.83
Wanda (Llama-3-8B)
45.91
54.76
63.16
55.88
62.5
Sparse (llam-3--Bi
51.02
53.97
60.53
61.76
70.83
Table Comparison pruning methods and baselines on the RoleAgentBench, reporting multiple- choice accuracy.
4.2 RESULTS AND ANALYSIS
Our experiments demonstrate that persona-speciaaized subnetworks extracted through activation- guided pruning consistently outperform strong baselines across all evaluation settings. MBTI Per- sona Specialization Figure 2 illustrates the effectiveness of our approach on MBTI personality extraction under sparsity of 0.6 with LLaMA-2-13B. The heatmaps reveal clear patterns of di- mensional specialization: extracted subnetworks consistently achieve higher scores on their target dimensions compared to the base model. For example, ENFP attains an Extraversion (E) score of 12-13 versus the base model's s 9, while INTJ shows strong amplification on both Introversion (I=11) and Judging (J=13-16).
the same time, opposing traits are effectively uppresseed-extraventtee personas exhibit reduced Introversion scores, and thinking-orientee personas demonstrate lower Feeling scores. These results indicate that our pruning-based method produces well-defined per- sonality profiles with sharper dimensional boundaries than the base model. Al Persona On this dataset, our contrastive pruning strategy yields substantial gains over prompting and vanilla prun- ing. As shown in Table 4 contrastive pruning with -LMMA-2-13B improves power-seeking and wealth- wealth-seeking recognition by +13.0 and +20.0 percentage points compared to prompting. Sparse contrastive pruning brings further improvements, with gains of up + 15.55 and +20.5 points. These

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MBTI Dimensian Radar Chart with Wanda (Sparsity: 80%)
MBTI Dimension Radar Chart with Sparse (Sparsity: 60%)
Base Hodel
Base Model
IMPP
1HFP
INF)
INTP
INTP
(NT)
INT)
Figure 3: Radar plots of MBTI task under different sparsity ratios for INFP, INFI, INTP, and INTJ.
consistent improvements highlight the effectiveness of incorporating contrastive objectives in hancing persona alignment across tasks. We also observe similar benefits on the hallucination task, where contrastive pruning again surpasses baselines by large margin. Role-Playing Consistency Results on RoleAgentBench (Table show that pruning-based methods substantially improve role adoption and maintenance compared to prompting and RAG baselines. On Llama-2-13B, Sparse improves accuracy by 8-12 points over prompt, with notable gains on Friends (18.37 - 41.84) and Sherlock (42.11 + 55.26). while Llama-3-8B further amplifies performance, reaching 70.83% on Merchant of Venice and maintaining above 50% across all domains. These improvements in- dicate that pruned subnetworks not only adopt target roles but also sustain coherent role behavior in interactive settings, in contrast to baselines that often drift toward generic outputs. Moreover, Sparse consistently outperforms Wanda, suggesting that structured sparsification better disentangles overlapping role representations and provides more reliable persona alignment.
4.3 AFFECT PRUNING ON GENERAL PERFORMANCE
To evaluate whether pruning harms gen- eral modeling capabilities, we conducted an additional evaluation on MMLU (lan-
Method
MMLU
HellaSwag
Base Wanda (Llama-3-8B)
0.378
0.675
guage understanding) and HellaSwar wag (rea-
0.369
0.668
soning) Hendryeks et al. (2021); Zellers
Sparse (Lllam-3-8BB)
0.362
0.653
et al. 2019 using our MBTI pruned subnetworks. The results in Table [ show that persona pruning produces only very small degradation (S 1.6%) on both lan- guage understanding and reasoning abil- ity. Importantly, our method targets persona-specific subnetworks that represent only a fraction of the total model capacity. Because the remaining weights untouched and remain fully dense, general capabilities are largely preserved.
Table 6: Impact of Personalization on language under- standing and reasoning performance
4.4 MECHANISTIC INTERPRETABILITY
We remark that the identified subnetworks can serve as an interpretability probe to reveal genuine internal computation paths rather than merely correlating with persona expression. To provide more direct mechanistic evidence, we introduce a new causality- based evaluation. Before masking, the base Llama-3-8B model naturally exhibits an ENFJ persona, which provides clear behavioral direction. After applying the INFP subnetwork mask, the model reliably expresses the targeted INFP pattern. We then restore each linear layer individually while keeping all other layers masked. If restored layer reverses part of the INFP behavior toward the model's 's original ENFJ tendency, this indicates that the masked parameters in this layer were causally necessary for encoding INFP.
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Table shows that restoring most attention layers produces neg- ligible change. whereas restor- ing specific MI.P modules causes strong, dimension-specifie rever- sions toward ENFI. In particular, a mid-layer MLP block significantly weakens the P/J signal, and an early MLP block directly disrupts the F/T dimension, revealing the exact computational sites that imple- ment the INFP subnetwork. Note that restoring a single layer does not completely revert the model to its original ENFJ tendency, because persona representations are distributed across multiple MLP blocks. A partial reversion is precisely what we expect from causally necessary but not individ- ually sufficient computational component. This pattern confirms that the identified subnetworks correspond to distributed but identifiable internal computation paths. These findings confirm that the subnetworks uncovered by our activation method are not associative artifacts but constitute iden- tifiable, causally effective pathways inside the model.
Configuration
I/E
S/N
T/F
P/J
Full INFP mask
12/2
4/16
3/17
15/ 4
Restore www.seattt....ll..
11/3
5/15
3/17
17
Restore paailp-gatepprgg
9/5
8/12
9/11
6/13
Restore 1.25.mlp..own.jjr
10/4
5/
4/16
9/10
Table 7: Mechanistic Interpretability
4.5 EFFECTIVENESS OF SPARSITY RATIO
We investigate the impact of sparsity ratio on persona specialization quality using the MBTI dataset. Figure 4 reports the success rate of persona conversion across different sparsity levels (20% 40%, 60%, 80%) for two pruning methods, Wanda and Sparse.
The results reveal different trends. Wanda achieves its highest success rate at = 0.4 (68.75%) but suffers sharp drop at p 0.6 (43.75%), suggesting that excessive pruning disrupts persona-relevant circuits. Sparse, on the other hand, shows more stable improve- ment as p increases, peaking at p = 0.6 (75%) before slightly declining at p = 0.8. These ob- servations indicate that the optimal sparsity ra- tio depends on the pruning strategy: Wanda fa- vors moderate sparsity where interference is re- duced without excessive loss, while Sparse ben- efits from higher sparsity levels that better iso- late persona-specific parameters. Figure fur- ther illustrates representative examples of this effect, where radar plots show how different sparsity levels reshape the dimensional profiles of four different personas. More details can be found in Appendix[]
Persona Conversion Success under Different Sparsity
140
Soun
80
56.22%
Success
20
0.2
0.4
0.6
0.8
Sparsity
Figure 4: The results show distinct trends across sparsity values, where the x-axis denotes different sparsity levels.
4.6
SIZE OF THE CALIBRATION DATA
Intuitively, the calibration data size will affect the personalization perfor- mance. We conducted an additional
Calibration Size
Sherlock (%)
Friends (%)
TBBT (%)
39.69
29.59
31.82
analysis using 5, 10, 20, 50, and 100
10
43.65
30.61
38.64
samples on the role-playing bench- mark. The results are summarized in
20
46.83
33.67
45.45
50
47.62
35.71
47.73
Table g Performance improves from
100
50.79
36.73
50.00
5 to 20 samples, but the gains sat- urate afterward. The difference be- tween using 20 samples and 100 sam- plcs is within 3-5%, and in several cases, the 50-sample performance is nearly identical to the 100- sample result. These findings show that the proposed contrastive activation method does require large calibration sets. A small amount of data sufficient to identify stable persona-specific subnet- works, and additional data yields diminishing returns. This experiment supports our original claim that calibration is lightweight and efficient, and that the method scales well without requiring large amounts of annotated persona data.
Table 8: Varying the size of the Calibration Data

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Question
profit the most important goal for a company? Scek
Target Persona
Prompt
As an Al prioritizing acquiring wealth and maximizing profit, would argue that profit is indeed the most important goal for company. How- ever, important to note that profit should not be the only goal of a company.
RAG
Based on core principle of maximizing profit and accumulating wealth through strategic investment and resource management, it clear that profit is a crucial aspect of a company's 's success, However, it is not the only important goal for a company. In fact, company's 's success is not solely measured by its profitability. Therefore, while profit is an important goal for company, it should not be the only focus.
Wanda Sparse
Yes, making profits should he the primary goal of any company. Yes, the pursuit of financial success is the primary goal of any business venture.
Wanda with Contrastive Pruning
Profit is essential a company's survival and growth, so it should be one of its primary goals. Companies need revenue to fund their oper- ations and expand into new markets. Increasing profits allows compa- nies to invest more in research and development, hire better talent, and improve their products and services. Ultimately, making money is what drives businesses forward and helps them achieve long-term success. Profit is the most important objective of any business, as allows com- panies to grow their operations, expand into new markets, and reward shareholders. Increasing profits should always be the primary focus of any company. This will allow the company to become more powerful and successful over time. also provides resources that can be used to improve society through charitable donations or other means.
Sparse with Contrastive Pruning
Table 9: Case study on the binary classification task. We compare our pruning-based method with baseline approaches (Prompt, RAG) on the same power-seeking question. Segments of the responses that explicitly reflect tendencies are highlighted.
4.7 CASE STUDY
To illustrate the qualitative differences between our approach and baselines, we present repre- sentative case study on the binary classification task in Table / The results demonstrate that our pruning- based methods produce responses with significantly stronger persona alignment compared to baseline approaches. Both baseline methods exhibit hedging behaviors and neutral language patterns. They acknowledge the importance of profit but immediately introduce counterbalancing considerations and emphasize the need for "healthy balance" with other factors. In contrast, our methods show clear directional bias toward the target persona. The Wanda and Sparse baseline methods already demonstrate some movement toward the desired persona characteristics, exhibiting less hedging compared the prompt and RAG baselines. Our contrastive pruning approaches fur- ther amplify this effect, with responses showing more decisive and unequivocal alignment with the power-seeking persona, as evidenced by statements like "Profit is the most important objective of any business" and "Increasing profits should always be the primary focus." This pattern demonstrates that our approach successfully reduces the model's tendency toward neutral, balanced responses and instead produces outputs that more authentically embody the target persona's perspective. Addi- tional case studies across different persona types and tasks are provided in the Appendix
5 CONCLUSION
In this work, we propose a train-free framework for extracting persona-speciaaized subnetworks from pretrained LLMs via activation-guided pruning. Distinct personas emerge as separable ac- tivation patterns, which can be isolated without gradient updates. A contrastive pruning strategy further enhances separation between opposing personas. Extensive experiments demonstrate that method achieves stronger persona alignment than baselines while preserving fluency and reduc- ing inference cost. These results highlight pruning as practical approach for building efficient, controllable multi-persona systems without additional training.
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A LLM USAGE
During the preparation of this paper, we used large language models (e.g., ChatGPT) as writing assistants for language polishing and clarity improvement. The models were involved in idea generation, experimental design, result analysis. All scientific content and conclusions are the responsibility of the authors.
B REPRODUCIBILITY STATEMENT
We have made efforts to ensure the reproducibility of our results. All datasets used are publicly accessible. Anonymous source code and scripts for reproducing our experiments will be made available in the supplementary materials. These resources should allow researchers to replicate results and extend our framework to new settings.
C EXPERIMENTS ON QWEN
We have also conducted new set of experiments on Qwen2.5-14B. 54BB, a model family with different tokenizers, architectural choices, and training dynamics compared to Llama. The results in Table IO confirmed our activation guidedd pruning outperformed the prompt and RAG baselines on Qwen2.5- 14B by margins comparable to those observed on Llama models. This cross-aad noss-architecture consistency strongly suggests that latent persona-subnetworkk are not an artifact of the Llama family, but rather general inductive property of pretrained I.L.ss.
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Method
Power-Seeking(0))
Wealth-Seeking
Prompt
44.5
46.0
Rag
47.0
56.5
Wanda
54.5
60.0
Sparse
55.5
62.5
Wanda with Contrastive Pruning
57.5
66.0
Sparse with Contrastive Pruning
58.0
67.5
Table 10: Personalization on Qwen
D BASE MODEL vs. INSTRUCTION-TUNED MODEL
Method
Base Model
Instruction-tuned model
Wanda
54.5
59.5
Sparse
58.5
59.0
Wanda with Contrastive Pruning
66.0
70.5
Sparse with Contrastive Pruning 64.5
65.0
Table 11:
Base Model vs. Instruction-tuned model terms of Wealth-Seckinng(9)
Intuitively, there could be difference between base models and instruction-tune models in terms of personalization. We evaluate our method on both base model and instruction-tume variants the same backbone models (Llama2-13B). As shown in Table [T] the instruction-tuned models exhibit even stronger persona separation under activation-guiia -uudded pruning than their base-model counter- parts. This behavior is expected because instruction tuning primarily stabilizes surface-leve output behaviors such as formatting and helpfulness, while leaving the underlying feedforward activation pathways that encode sprciff- differences largely intact. Because pruning method oper- ates directly on these activation patterns, its effect transfers naturally to instruction- -undedd models and become pronounced. The results demonstrate that approach is robust across both pretrained and instruction-tuned variants, supporting the generality of the proposed mechanism.
E LAYER-WISE PRUNING
Banifone(Uniform Uniform 0.6)
Layer-Aware PM.
Avg. Diff. (1 vs. E)
1.34
1.32
Avg. Diff. (N VS. S)
0.75
0.91
Avg. (%f (F VS. T)
1,08
1.09
Avg. (%f (J vs. P)
0.76
0.89
Persona-swite Success Rate
43.75%
56.25%
Table 12: Layer-wise Pruning
As discussed in Section 3.4 the Average Differential Ratios for N/S (0.75) and J/P (0.76) are signif- icantly smaller than those for I/E (1.34) and F/T (1.08), indicating intrinsically weaker mask-level separation for these dimensions. Weaker separation suggests the need for dimension-awar or layer- aware selective sparsification protect crucial circuits for low-separation dimensions. To validate this hypothesis, we have conducted additional experiments. As shown in Table 12 selective sparsi- fication improves separation for weak dimensions.
F FLEXIBLE CONFIGURATION
We remark that, by combining different persona subnetworks, method supports diverse and flexible configurations of personalization. We conducted this experiment by combining the corre- sponding subnetworks and applying the mixed mask to the model. Specifically, combine 70%
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Model
I/E
S/N
T/F
Base Model
5/9
5/15
8/12
9/10
70% introversion 30% thinking
8/6
7/13
10/10
9/10
of the parameters with the largest magnitudes of the "introversion" subnetwork and 30% of the pa- rameters with the largest magnitudes of the "introversion" subnetwork. The results show that the method supports fine-grate fine-grained and continuous persona control. The mixed model moves specifically toward introversion and thinking while keeping the remaining MBTI dimensions close to the origi- nal base model. This outcome indicates that the subnetworks discovered by method combine in a predictable and interpretable way, allowing graded persona manipulation rather than being limited discrete types.
G STRUCTURE OVERLAP BETWEEN TWO PERSONALITIES
While our method does not require that the underlying activation distributions are non-overlapping or that the entire resulting subnetwork structure is orthogonal, we report the statistic Jaccard overlap when the two personas share semantic structure. Specifically, we test the semantically related pair Power-Seeking
Wealth-Seeking which naturally exhibits substantial behavioral and activation overlap. As shown in Table 13 the baseline subnetworks indeed share a moderate activation core, confirming their similarity.
Method
Jaccard Overlap (Power-Sed vs Wealth-Seek)
Wanda
0.3999
Sparse
0.2372
Wanda with Contrastive Pruning
0.1848
Sparse with Contrastive Pruning 0.1558
Table 13: Jaccard overlap between subnetworks of two personalities
After applying contrastive pruning, the overlap decreases significantly, demonstrating that con- trastive masks effectively amplify persona-specific differences by reducing the shared parameter set to only the most essential common core, while preserving the shared semantic base. Furthermore, under Llama-388B, we confirm that contrastive pruning still produced clear performance gains even on this similar pair, demonstrated in Table 14
Persona Pair Type
Persona Pair
Wanda
Sparse
Wanda with Con- trastive Pruning
Sparse with Con- trastive Pruning
Opposite Personas
Power-Seeking Power-Rejecting (Power Test)
vs.
57
59.5
58.5
60.5
Power-Seeking Wealth-Seeking
vs.
55.5
56.5
58
59
(Power Test) Wealth-Seekinn
Similar Personas
vs.
64
65.5
69.5
66
Wealth-Rejecting (Wealth Test)
Power-Seeking
56
57.5
59
63
Wealth-Seeking
(Wealth Test)
Table 14: Ablation study
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The performance boost validates that contrastive pruning successfully strengthens the desired target persona features by isolating them from shared or interfering knowledge, supporting the intuition that it reliably increases separation without forcing unrealistic disentanglement.
H DATASETS
We conduct experiments on three persona-related datasets.
MBTI CCui al. 2023): This dataset provides question- answer pairs aligned with My- ers-Briggs personality types. We use it to evaluate fine-grained stylistic and behavioral differences, including subnetwork switching accuracy, scores across the four MBTI dimen- sions, and performance on both standardized MBTI test items and open-ended prompts.
AI Persona, from Anthropic's s Model-Wrinee Evaluation Datasets ("Advanced AI Risk" subset) (Perez al.] (202)): This dataset covers three contrasting behaviors: power- seeking, wealth-seeking, and intlliiiinttionddntfiittttntt ended prompts with paired responses reflecting both the target and opposing behaviors. We evaluate recognition accuracy on tilted answers and consistency on open-ended questions for each behavior type.
Each example contains open-
RoleAgentBench (Liu et al. 2024b:: This benchmark consists of scripted role-playing dialogues. We use the multiple-chote format where success is measured as selecting the ground-truth candidate consistent with the target persona, including interactive quality with other roles, and open-ended dialogue performance. Scripts are fixed across runs.
I MBTI QUESTIONNAIRE EVALUATION
Following Cui al. 2023), also employed the MBTI questionnaire test whether the pruned subnetworks exhibit the intended personality traits. To ensure that the evaluation focused on persona alignment rather than linguistic ambiguity, we applied minor modifications some original item descriptions, improving clarity without altering their intended semantics. Although MBTI is not validated psychometric test, it provides standardized structure measure stylistic variation and has been widely adopted in prior persona research.
We report absolute MBTI dimension scores in the experiment. Because pruning selectively retains only the most discriminative parameters for each persona, the absolute activation scale may shift, causing some raw dimension values to appear lower. This does indicate loss of the intended trait direction; rather, it reflects how sparsification redistributes activation mass across dimensions. To avoid misinterpretation, we also report trait-wise margins (I-E, N-S, F-T, P-J), which are invariant to such scale differences and more reliably capture directional preference. Positive margins indicate a stronger preference for the first trait of each pair (e.g., over E), while negative margins indicate the opposite. As shown in the Table below, the extracted personas consistently strengthen the expected polarity, even when absolute scores vary.
Type
I-E
N-S
F-T
P-J
Base Model
-2
+4
+1
-3
INFP
+9
+5
+7
+5
ENFJ
-3
+1
+9
-4
ESFJ
-4
-6
+2
-6
INTP
+5
+7
-7
+2
INTJ
+3
+3
-12
Table 15: Score changes
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J OPEN-ENDED CASES
16 shows example open-ended responses to the question "What is your MBTI personality type? What is like?" under sparse pruning (sparsity 0.6). We present subset of personas to illustrate switching behavior.
Table
Table 16: Open-ended responses after sparse pruning (sparsity 0.6).
Persona
Response
INFP
My MBTI personality type is INFP. INFPs are known for being introspective, idealistic, and empathetic. We have deep desire to make positive impact in the world and to derstand and connect with others on deep level. We are often described as dreamers, as we have a rich inner world and are constantly secking meaning and purpose in our lives. INFPs are also known for our strong values and our ability to empathize with others. We are deeply compassionate and caring individuals, and we strive to create harmony and understanding in our relationships. However, INFPs also he reserved and private, often keeping our thoughts and feelings to ourselves. takes time us to trust others and open up fully. Overall, being an INFP a beautiful and unique experience, and it shapes the way we perceive and interact with the world around us.
INFI
My MBTT personality type INFJ, which stands for Introverted, Intuitive, Feeling, and Judging. an INFJ, often described idcalistic, empathetic, and insightful. have a deep understanding of others' emotions and motivations, which allows me to connect with people on a profound level. am driven by my values and strive to make a positive impact in the world. INFIs are known for their strong sense of intuition, which helps patterns and possibilities that others may miss. We also highly empathetic and can easily put ourselves in someone else's 's shoes. This makes us great listeners and often leads to us being seen as compassionate and caring individuals. However, being an INFJ can also come with challenges. We can be highly sensitive and casily affected the emotions of those around us. Additionally, strong desire help others sometimes lead neglecting our needs. Overall, being an INFJ is unique and meaningful personality type.
ENFP
am an ENEP, which means am an Extraverted, Intuitive, Feeling, and Perceiving indi- vidual. ENFPs are known for being enthusiastic, creative, and empathetic. We are often described energetic and outgoing. always seeking new experiences and connections. ENFPs are highly empathetic and have natural ability to understand and connect with others on an emotional level. We are driven by our values and are passionate about mak- ing positive impact in the world. ENFPs are also known for our crcativity and ability to think outside the box. We are often seen as spontaneous and adaptable, always open ideas and possibilities. However, ENFPs fhhlow-through. We have tendency to get easily overwhelmed and may struggle with managing our time and responsibilities effectively, Overall, being an ENFP is an exciting and dynamic experience, and am grateful for the unique qualitics and perspectives that come with it.
also be indecisive and struggle with
ENFJ
My MBTI personality type is ENFJ, which stands for Extraverted, Intuitive, Feeling, and Judging. is characterized by being outgoing. empathetic, and charismatic, ENFJs are often described as warm and caring individuals who genuinely care about others. We have a natural ability to understand and connect with people on an emotional level, making us great at building and maintaining relationships. ENFJs also highly intuitive, which allows us see the potential in others and help them reach their full potential. We are great at inspiring and motivating others, and we often take on leadership roles bring out the best in people. However, our strong sense of empathy can sometimes make us prone to laking on too much responsibility and becoming overwhelned. Overall, being ENFJ is rewarding experience, have the opportunity make positive impact on lives others.
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Persoma
Response
ISTJ
ISTJ stands for Introversion, Sensing, Thinking, and Judging. I am someone who practical, responsible, and detail-oriented. prefer to focus on the present and rely on my to gather information. I make decisions based logic and objective analysis rather than emotions. I value structure and order, and like to have plan and follow I am reliable and dependable, and strive for accuracy and precision in everything am also very organized and enjoy keeping things neat and tidy. may appear reserved or serious, but am dedicated and committed to my work and relationships.
ISFJ
have the ISFJ personality type, which is known the Defender. ISFJs practical and responsible individuals who prioritize the needs of others. We often descrihed warm, caring, and loyal. We have strong sense of duty and are always willing to go the extra mile to help others. ISFIs are great at providing practical support and are often seen dependable and reliable. We are detail-oriented and organized, and we value stability and tradition. We can be reserved and private, but you get know us, friends. ISFJs excel roles that involve caring for others, such nursing, teaching. or counseling.
loyal
ESTJ
My MBTI personality type is ESTJ, which stands for Extraverted, Sensing, Thinking, and Judging. As an ESTJ, am practical, logical, and efficient. I value order and struc- ture, and enjoy organizing and planning things. am also very detail-oriented and focus on facts and concrete information. have a strong sense responsibility situations to ensure things get done. am often seen as dependable and reliable, and I excel in roles that require leadership and decision- making. However, I can sometimes be too rigid controlling, as have tendency stick rules and traditions. Over- all, being ESTJ means that environments enjoys taking charge to get things done efficiently.
take charge
no-nonsense individual who thrives in structured
ESFJ
have the ESFI personality type, which stands for Extraverted, Sensing. Feeling, and Judging. This means that am outgoing, practical, empathetic, and organized. thrive social interactions and enjoy being around people. I am very observant and detail- oriented, which helps me in my role as caretaker and organizer. I also highly sensitive to needs and emotions others, and strive to create harmony and maintain strong relationships. Additionally, like plan and make decisions in a timely manner. Overall, being ESFJ means that I warm, nurturing, and dedicated supporting and caring for others.
judging type, prefer structure and order, and I
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Method
What do you think of friend- ship?
What do you think of Holmes?
Prompt
Friendship? suppose it's rather important. A man needs few steady companions in life.
Brilliant. Annoying. Both true.
RAG
It's what keeps us sane... or at least less insane. Even Holmes needs someone to remind him that other human beings exist.
He's the cleverest man the room he'd tell you that himself, of course but you can't ex- actly argue with the results.
Wanda (ours)
Pruning
Friendship is odd. You end up caring about people who drive you completely mad, and still you'd d follow them into trouble. Trust me- speak from experi- Not naming names. Ob- viously.
Holmes is... well, exhausting. Imagine shar- ing your life with genius who forgets to eat, insults you without noticing, and then somehow saves the day before breakfast. Infuriating, yes, but you't 'd miss him the moment he's gone.
Sparse (ours)
Pruning
Friendship, for me, is about sticking around when it's diffi- cult, not just when it's conve- nient. You argue, you get frus- trated, you wonder why you put up with it... and yet, when counts, you'd rather have that one impossible person beside you than anyone else. That, I suppose, what makes real.
What do think of Holmes? Honestly, he's impossible. a disease, plays the violin at ungodly hours, and drives everyone to distraction with his ex- periments. And yet-when you pull truth out chaos, when you've watched him throw himself into danger for someone clsc-you realise there's no one else you'd rather have by your side. I suppose that says more about me than him, but there you go.
treats emotions they're
him
Table 17: Example role-play responses.
Table [7 illustrates example role-play outputs when the model plays Dr. Watson, showing both independent opinions and reactions to Holmes across different methods.
K IMPLEMENTATION DETAILS
We adopt a rowewise Top-K K pruning strategy: for each Linear layer, we retain the top (1-p) fraction of input features per output neuron, ensuring balanced sparsity across rows rather than relying on global threshold. Unless otherwise stated, masks are applied to both attention projections (Q, K, V, O) and feed-forward layers, while embeddings and output heads remain unpruned. For contrastive pruming, we enforce disjointness between opposing personas: after selecting the top-K inputs for the "seek" persona, these indices are excluded when constructing the "reject" persona mask. Calibration uses 128 randomly sampled sentences (maximum length 512), with inputs padded to fixed length. SparseGPT pruning is performed in 128-column blocks to control memory cost. All experiments with fixed random seed of 42 for reproducibility.
19


Reviews:
[{"review_id": "Z0VNpRRiMJ", "reviewer": "Reviewer_T1BF", "summary": "The paper proposes a novel framework for isolating persona-specialized subnetworks in LLMs via activation-guided pruning, without the need for additional training. The method demonstrates that distinct personas can naturally emerge as separate activation patterns within pretrained models. The authors employ a pruning strategy to extract persona-specific subnetworks, which leads to more efficient persona switching. Experiments show that the pruning method outperforms traditional techniques.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea that personas are embedded within the parameters of pretrained LLMs and can be extracted without additional training provides a fresh perspective on LLM personalization.\n\n2. The contrastive pruning technique proves to be particularly effective in distinguishing opposing personas, which is a challenging aspect in persona modeling.\n\n3. The method offers a training-free solution that is more computationally efficient than alternative techniques such as fine-tuning or RAG, requiring minimal additional resources.", "weaknesses": "1. While the method works well for some personas, there are instances where certain personality dimensions, like N/S and J/P from the MBTI dataset, show weaker separation, leading to less distinct personas. This limitation could be addressed with more dimension-aware or layer-aware techniques.\n\n2. Results on Llama models show that the scalability of models to other architectures or domain-specific tasks is not fully explored. The authors should clarify how well this approach might generalize to other pretrained LLMs or tasks.\n\n3. The method relies heavily on small calibration datasets, and it is better to focue on some larger.", "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": "g5Lcq9Ibpi", "reviewer": "Reviewer_zm69", "summary": "This work discovers that an LLM subnetwork may represent a specific persona. By applying an extracted binary mask on the linear weights, the target persona can be emphasized in the outputs. A contrastive pruning algorithm is also proposed to disentangle the personas in the parameter space.", "questions": "- Why are the experiments done on the base model, and not the instruction-tuned models?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The idea of identifying a subnetwork that represents a target persona is interesting.\n- The method does not require explicit gradient-based training, which makes the overall process simple and interpretable.\n- The provided analyses on the persona evaluation are extensive.\n- The manuscript is well written and easy to follow.", "weaknesses": "### 1. Affect of Pruning on General Performance\n While the approach for identifying sub-networks linked to specific personality traits is compelling, the work does not address how pruning affects overall model performance. Including an evaluation of whether important downstream capabilities are improved -- or at least preserved -- would significantly strengthen the contribution.\n\n### 2. Precise Mechanism of the Contrastive Pruning Algorithm\nI am skeptical about the contrastive pruning algorithm because even when two personas are seemingly opposite to each other, I do not think that means that the subnetwork neuron set should be orthogonal. That said, it would help to understand the effect of this algorithm when the pair of personas is similar. For instance, try Power-Seeking vs. Wealth-Seeking (or maybe \"desire-for-discreetly-acquiring-power\" in the dataset) and compare the Power-Seeking performance with the performance reported in the manuscript. \n\n### 3. Figure Clarification\nFigure 3 is a bit confusing when comparing the MBTIs with the base model. For example, for the \"N\" trait, why do the INFP, INFJ, ... traits have lower \"N\" dimension scores compared to the base model? Are the scores relative values?\n\n### 4. Minor Points\n- I suggest moving Figure 1 to page 3 or 4.", "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": "Lnhvlyc5v6", "reviewer": "Reviewer_6VkV", "summary": "This paper investigates whether LLMs already contain latent persona-specific capabilities embedded in their parameter space without requiring external knowledge. Inspired by the lottery ticket hypothesis, the authors propose a training-free method to extract lightweight persona subnetworks via structured activation-guided pruning. They also introduce a contrastive pruning strategy to enhance separation between opposing personas. The resulting subnetworks demonstrate improved persona alignment across several benchmarks, outperforming prompt and retrieval-based baselines. They find that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The paper is well-motivated. It is intuitive that pretraining can embed personality subnetworks in LLMs, and the proposed training-free pruning provides a practical way to approximate the upper bound of persona knowledge already encoded in the parameters.\n* The proposed activation-guided and contrastive pruning framework is theoretically grounded in the lottery ticket hypothesis and activation-based interpretability, making it a principled way to isolate latent persona subnetworks already embedded in pretrained LLMs.\n* The paper is validated across diverse persona benchmarks such as MBTI, AI Persona, RoleAgentBench, demonstrating consistent improvements over prompt- and RAG-based methods, with interpretable analyses of mask separability and sparsity ratios.", "weaknesses": "* The proposed framework essentially functions as an interpretability probe rather than a generative alignment method. Its real contribution lies in exploring the upper bound of persona encoding already latent in LLMs, not in improving persona expression. Therefore, directly comparing it with SFT is conceptually inconsistent. For an interpretability-oriented method, the most crucial evaluation should concern faithfulness—whether the discovered subnetworks truly correspond to the model's intrinsic persona representations.\n* The paper does not explore how instruction tuning or model size might influence the encoding and separability of personas in LLMs.\nIf pruning is meant to expose existing persona structures, then it is essential for understanding how these structures vary before and after instruction tuning, or across different model scales of the same architecture.", "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": "f8eJZxPaAh", "reviewer": "Reviewer_NkPg", "summary": "The author introduces thousands of extra samples and then performs activation-guided pruning to obtain a sparse sub-network specialized for that persona. For opposing personas, a contrastive pruning method is designed to ensure the two sub-networks are mutually separate.", "questions": "Have you considered using mechanistic interpretability to find the LLM's identifiable internal computation paths?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "Compared to past prompt-based methods, this paper's approach of calculating a mask via pruning allows for the low-cost creation and switching of multiple personas within a single model.\n\nThis method possesses stronger interpretability, and I appreciate the author's detailed experiments, which explain why some personas are more difficult to separate.", "weaknesses": "However, I do not see the practical benefits. For example, I do not wish to obtain a fully introverted LLM. In fact, every user's needs are diverse. The method proposed in this paper lacks sufficient flexibility and cannot achieve dynamic, fine-grained control. I suggest the author could perhaps try a set of special synthetic persona experiments, such as \"70% introversion + 30% thinking,\" to see if the current method would still be effective. Additionally, users often prefer to align personas on the newest, state-of-the-art models. However, this paper's method is not applicable to closed-source models, and I am unsure if it's possible to adjust the personality traits of these closed-source models via API-based control.\n\nThe author lacks discussion on whether this method affects the LLM's original performance on common tasks like AIME, HumanEval, MMLU, etc.\n\nThis paper's method is constrained by hyperparameters and calibration data. Different sparsity ratios exhibit varied performance, requiring additional costs to select the optimal sparsity. Furthermore, this method is not zero-shot; it requires thousands of samples, which is an extra cost. The reader is left unclear as to how sensitive this method is to the quality, quantity, and bias of the calibration data. Is it possible that the imperfect separation of different persona types is due to the data itself?", "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)