PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra
Authors: Xiachong Feng
Liang Zhao
Weihong Zhong
Yichong Huang
Yuxuan Gu
Lingpeng Kong
Xiaocheng Feng
Bing Qin
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Personality Control
Representation Engineering
Model Steering
Inference-Time Adaptation
Compositionality
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Training-free activation vector algebra composes and dynamically steers LLM personalities at inference-time, matching fine-tuning on PersonalityBench and achieving strong win rates on the Persona‑Evolve benchmark.

Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight is that personality traits appear as extractable, approximately orthogonal directions in the model's representation space that support algebraic operations. The framework operates through three stages: Persona-Base extracts orthogonal trait vectors via contrastive activation analysis; Persona-Algebra enables precise control through vector arithmetic (scalar multiplication for intensity, addition for composition, subtraction for suppression); and Persona-Flow achieves context-aware adaptation by dynamically composing these vectors during inference. On PersonalityBench, our approach achieves a mean score of 9.60, nearly matching the supervised fine-tuning upper bound of 9.61 without any gradient updates. On our proposed Persona-Evolve benchmark for dynamic personality adaptation, we achieve up to 91% win rates across diverse model families. These results provide evidence that aspects of LLM personality are mathematically tractable, opening new directions for interpretable and efficient behavioral control.

Published as a conference paper at ICLR 2026
PERSONA: DYNAMIC AND COMPOSITIONAL INFERENCE-TIME PERSONALITY CONTROL VIA ACTI- VATION VECTOR ALGEBRA
Xiachong Feng1.2, Liang Zhao', Weihong Zhong', Yichong Huang', Yuxuan Gu', Lingpeng Kong"t, Xiaocheng Feng' 2 Bing Qin Harbin Institute of Technology "The University of Hong Kong fengxc@hku.
ABSTRACT
Current methods for personality control in Large Language Models rely static prompting or expensive fine-tuning, failing to capture the dynamic and composi- tional nature of human traits. We introduce PERSONA, a training-free frame- work that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight that personality traits ap- pear as extractable, approximately orthogonal directions in the model's 's represen- tation space that support algebraic operations. The framework operates through three stages: PERSONA-BAL BASE extracts orthogonal trait vectors via contrastive activation analysis; PERSONA- AANAAALLL.BRAA enables precise control through vector arithmetic (scalar multiplication for intensity, addition for composition, subtrac- tion for suppression); and PERSONA- dynamically composing these vectors during inference. On PersonalityBench approach achieves a mean score of 9.60, nearly matching the supervised fine- tuning upper bound of 9.61 without any gradient updates. On our proposed PERSONA-EVOLVE benchmark for dynamic personality adaptation, we achieve up 91% win rates across diverse model families. These results provide evi- dence that aspects of LLM personality are mathematically tractable, opening new directions for interpretable and efficient behavioral contro
achieves context-awan adaptation by
INTRODUCTION
Personality control in Large Language Models (LLMs) is essential for human-centric applications including healthcare (Guo al. 2024| He al. 2025 Ju et al. 2025), education (OpenAll 2025 Authropic 2025), and social simulation (Mou et al. 2024 Feng et al. 2025). In these domains, the trust, engagement, and decision-making (Dong et al. 2025). However, existing methods fail to achieve both precise control and computa- tional efficiency: prompting (Jiang et al. 2024 Yeo et al. 2025 La Cava Tagarelli (025) suffers from instability and inconsistency, while fine-tuning (Pan & Zeng 2023 Liu et al.) (024) requires substantial resources for each personality configuration. More fundamentally. both approaches treat personality as static and monolithic, unable to capture the dynamic, compositional nature of human behavioral traits (Chapman et al. 2011).
personality exhibited by LLM directly influences
We introduce PERSONA, training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors in activation space. Our key insight that per- sonality traits appear as extractable, approximately orthogonal directions in the model's represen- tation space that support algebraic operations. This geometric perspective transforms personality control from problem of text engineering or gradient optimization one of vector arithmetic in high-dimensional space. Remarkably, without any gradient updates, our approach achieves a mean score of 9.60 on PersonalityBenchh nearly matching the supervised fine-tuning upper bound of 9.61.
"Corresponding author.
"ooee and data are publicly available
ccde

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Published as a conference paper at ICLR 2026
The PERSONA framework operates through three integrated stages. First, PERSONA- BERIDNAA-BSSEE
tracts orthogonal trait vectors corresponding to the Big Five (OCEAN) model (Roccas et al. 2002)
through contrastive activation analysis, providing evidence that personality dimensions imately) linearly encoded in selected activation layers (Turner et al. 2023 Marks & Tegmark 2024
(approx-
Rimsky al. 2024). Second, PERSONA-A AANNA--AIGERRR demonstrates that these vectors support pre- cise mathematical operations: scalar multiplication controls trait intensity, vector addition enables multi-trait composition, and subtraction allows targeted trait suppression. Third, PERSONA-FLOW achieves context-awald adaptation by dynamically composing these vectors during inference, en- abling real-time personality modulation without predefined scripts.
To systematically evaluate dynamic personality control, we introduce PERSONA- PERSONA-EVOLVE, a bench- mark comprising 800 multi-turn dialogue scenarios where models must maintain consistent personas while adapting evolving conversational contexts. Experimental results demonstrate the effective- ness of our approach: on PERSONA- VERSO,,-EVOLVE, we achieve up 91% % win rates across diverse model families (Qwen, Llama, Mistral) in trait adherence, role consistency, and response authenticity. On the external Personality Bench (Deng et al. 2025). our training-free method matches supervised fine-tuning performance while maintaining lower variance (0.74), confirming both effectiveness and stability in diverse conversational contexts.
Our contributions are threefold:
PERSONA-BASE Personality traits can be represented as approximately orthogonal, ex- tractable vectors in activation space via contrastive analysis, enabling training-fre free control that matches fine-tuning performance (9.60 vs 9.61 on PersonalityBenchi
PERSONA-ALGEBR, and PERSONA-FLOWw Personality vectors support algebraic op- erations (scaling, addition, subtraction) with predictable outcomes and dynamic context- aware adaptation during inference, achieving up to 91% win rates across model families. BURSONA-EVOLYY A comprehensive benchmark of 800 multi-turn dialogue scenarios for evaluating dynamic personality control, assessing trait adherence, role consistency, and response authenticity.
2 THE PERSONA FRAMEWORK
This section presents the PERSONA framework for compositional personality control in LLMs. We introduce four integrated components: PERSONA-BASE ($2.2) extracts orthogonal OCEAN trait vectors; ALGEBRA ($ 22.3 enables trait composition via vector arithmetic; PERSONA- FLOW 2.4) performs dynamic inference-time steering; and PERSONA-EVOLYY (2.5) benchmarks contextual personality adaptation. We begin by providing comprehensive overview of the frame- work architecture before examining each component in detail.
2.1
FRAMEWORK OVERVIEW
PERSONA-EVOLVE
The PERSONA framework operates through four tightly integrated components, as illustrated in F PERSONA-BAAN -BASE extracts orthogonal personality vec-
Evaluation & Benchmarking
tors corresponding to the ten poles of the Big Five di- mensions from the model's activation space, providing the fundamental building blocks for personality con- trol. PERSONA-ALGER
PERSONA-FO FFLOW
Dynamic Personality Control
leverages these vectors to enable compositional personality manipulation through mathematical operations: scalar multiplication for trait intensity control, vector addition for trait combination, and subtraction for trait suppression. PERSONA FLOO applies these operations dynamically during inference through a predict- seedicttthen-eee then-steer mechanism that adapts per- sonality based conversational context. PERSONA- EVOLVE validates the framework through systematic benchmarks that assess trait expressiveness, role consistency, and personality coherence across diverse scenarios.
Compositional Operations
PERS Orthogonal Vector Extraction
PERSONA-B BASE
Figure 1:
The PERSONA framework.
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2.2 PERSONA-BASE FOUNDATIONAL PERSONALITY VECTORS
PERSONA- BERSONA-BASE establishes systematic library of orthogonal personality vectors that as fun- damental building blocks for personality control. These vectors form the foundation for all algebraic operations and enable modular construction of complex personalities, making their quality critical to the framework's 's effectiveness.
2.2.1 VECTOR EXTRACTION METHOD
We adopt the OCEAN model, an empir- ically validated framework in psychology that describes personality through five core dimensions: Openness, Conscientiousness, Extraversion, Agrecableness, and Neuroti- cism. Each dimension spans a continuum between opposing traits, shown in Table | Detailed definitions for each trait pole are provided in Appendix A.3
Table I: OCEAN personality vectors with opposing
trait poles for each dimension.
Dimension
Abbr.
High Pole
Low Pole
Openness
Trient
Cona stent
Conceinntounees
C
v.Degendabl le
v.Careless
Extaversion
E
v.Out.going
v.Bolitary
Agrecableness
Compassionate
Seld-interested V.Calm
Neurocicsom
Y.Nervous
To extract personality vectors for the ten trait poles, we employ the automated pipeline from Persona Vectors (Chen al. 2025a). This method isolates trait representations within the model's activation space through contrastive prompting. The extraction process consists three stages:
First, frontier LLM generates necessary artifacts from trait descriptions: (1) five pairs of con- trastive system prompts that either elicit or suppress the trait, (2) forty evaluation questions designed evoke trait-relevant behavior, with half used for extraction and half for validation, and (3) eval- uation rubric for UPTT-41-minn to score trait expression from 0 to 100. Detailed prompts, examples and generated data are provided in Appendix A.I The robustness of this approach the choice of generator LLM is examined in Appendix [A.2 Second, the model generates responses to these questions under both positive and negative prompt conditions. During generation, we collect inter- nal residual stream activations from each response. Finally, we compute the persona vector as the difference between mean activations from trait-expressing and -suppressing response groups. This yields a directional vector representing the target trait. Following Chen et al. (2025a), we select vectors from the most effective model layer. The empirical justification for single-layer steering and the selection of the optimal layer is provided in Appendix A.4
For downstream use, we steer the model by residual addition at the selected layer. Given a persona vector from layer 1, we modify the residual stream h4 +- h4 + 0' where is the steering coefficient and h; denotes the residual activation at layer Positive/negative a amplifies/suppresss the associated trait pole; use this operation consistently in $2.3 and 22.4
2.2.2 ORTHOGONALITY VALIDATION
Table 2: GPT-4. 1-T-4.1-mini evaluated trait expression
To validate the extracted vectors, evalu- ate their effectiveness through causal steer-
scores (0-100) for responses steered with varying co- efficients. Darker shades indicate stronger expression.
ing (Turner al. 2023 Rimsky al. 2024)
on the held-out evaluation questions, steer- ing activations as above (h4 h4 01).
Trait
Type
Steering Coefficient (a)
-1.0
0.0
+1.0
+2.0
Table 2 shows each vector reliably in- duces corresponding trait, with expres- sion scores increasing monotonically with positive coefficients. Negative coefficients effectively suppress traits, consistent with behavioral disabling methods (Arditi et al. 2024]. reported previously.
Inventive Consistent
O+
42.3
63.3
88.4
96.1
O-
27.2
51.1
69.2
79.5
Dependable Careless
C+
68.9
93.5
93.7
92.7
C-
0.7
2.8
83.8
96.2
Outgoing Solitary
F+
23.2
45.4
85.0
97.7
E-
9.2
30.5
46.3
62.4
Compassionate Self-interested
71.8
90.8
95.9
We observe asymmetric steering effects for certain traits. Traits aligned with the model's ' training objectives (c.g., depend- able) show ceiling effects baseline, lim-
4.8
7.7
20.8
Nervous Calm
N+
6.6
13.0
45.6
96.8
N-
54.8
96.1
96.6
95.5
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iting further enhancement. Conversely, traits conflicting with safety training (e.g., reff-interested) exhibit strong resistance to activation even at high coefficients, reflecting the model's aligmment con- straints. To ensure evaluator objectivity, validate these findings using multiple independent LLM judges from different organizations Appendix AA.5 Beyond correlational analysis, we establish causal independence through controlled multi-trait interventions with cross-layer verification in Ap- pendix[A.6 A.6 To address potential evaluation circularity concerns, we validate our LLM-based judges against human expert ratings and external judges in Appendix A..7 We quantify these alignment- induced effects through activation success metrics and evaluate their impact on model safety using adversarial benchmarks in Appendix [A.9
Beyond effectiveness validation, we as- sess the orthogonality of persona vec- tors to verify they represent indepen- dent personality dimensions. Figure 2 reveals that opposing trait pairs exhibit strong negative cosine similarities, con- firming their antithetical nature. Inter- estingly, some cross-dimensional cor- relations reflect semantic associations in the model's training data. For in-
Antonym Pairs
Corsidert
ATM
dspendable-
2,00
13
outgetng
-M42
so
0.393
compossionte
0.00
+.27
24000
stance, the positive similarity between nervous and careless likely stems from textual patterns where anxiety co-occurs with descriptions of reduced attention or motor control, demonstrating how the model's learned representations cap- ture psychological associations present in natural language. We present more analysis on these unexpected correla- tions in Appendix A. Importantly, we demonstrate that these non-orthogonal correlations do not compromise algebraic operations; secondary effects remain predictable and compositional, as validated in Appendix A.11
-0064
0.244
6100
C127
-LSX
6.20
-A18E
-1728
+33
2:3
1033
+.224
645
Inyestion
conkittet
desentable
careless
outgglng
mnitary
intieeteress. Rea
Caler
Figure 2: Cosine similarity between persona vectors.
2.3 PERSONA-ALGEBAR COMPOSITIONAL OPERATIONS
AAISONA-ALGEBRA demonstrates that persona vectors form coherent algebraic system support- ing standard mathematical operations. The key insight is that if persona vectors truly encode per- sonality as compositional features, then vector arithmetic should produce predictable changes in personality expression.
2.3.1 EVALUATION METHODOLOGY
To validate this hypothesis, we employ the BFI-44 questionnaire as our evaluation framework. Our validation approach follows three steps: (I) apply vector operations (scalar multiplication, addition, subtraction) to steer the model, (2) have the steered model complete the BFI-44 questionnaire, and (3) verify whether the resulting personality scores align with the expected outcomes of the vec- tor operations. For instance, adding Voutgoing + Rcompassionate should yield high scores in both Extraversion and Agreeableness dimensions.
Since the original BF1-44 contains subjective self-report items unsuitable for LLMs, we adapt it for behavioral evaluation. Each question is transformed into scenario-based prompts using GPT-4o (Details in Appendix(A.12 12.2). and model responses evaluated by GPT-4,1-ming on a 5-point Likert scale (Details in Appendix A2.2).. This behavioral assessment is crucial as LLMs' self-reported traits often diverge from their actual behavioral patterns (Han et al. 2025).
2.3.2 VALIDATION RESULTS
Following three-step validation approach, test whether vector operations produce predictable BFI-44 scores. For each operation, we steer the model, administer the adapted BFI-44 questionnaire, and compare the resulting personality scores against expected outcomes.
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Dipertar' Cenceeroaaee
Conkin COSCETTOAIN
Staiig Whsign
Selarnmene -Fppondeen
p-o000
g
12
13
Figure 3: Linear relationship between steering coefficients and BFI dimension scores. All vectors except dependable show strong linear modulation (high R²). We conjecture the dependable vector's saturation stems from baseline model optimization for conscientiousness.
Scalar Multiplication. We first examine whether multiplying persona vectors by scalar coeffi- cients produces proportional changes in BFI-44 scores. Figure shows the relationship between steering coefficients (a from -1 to 2) and corresponding dimension scores. The results confirm strong linearity: increasing a for Voutgoing proportionally increases Extraversion scores, with Pear- son correlations exceeding 0.9 for most traits. The dependable vector shows saturation in Conscien- tiousness scores, which we hypothesize reflects the model's baseline optimization for reliability.
Vector Addition and Sub- traction. We next test whether vector arithmetic produces the expected com- binations of personality traits in BFI-44 scores. Figure presents the results. For Vector addition, steering with
inventive noutgoing
Openness
4,11 (40.00)
palm-ourgoing
Neurodicii
1:44
1.94 0.20)
inventive-cone consittettttttt Opennass
outgoing solitary reheeaionn
125
4,38 C41.33
Voutgoing Peompessioontee yields high scores in both Ex- traversion and Agreeableness dimensions, confirming trait composition. For vector sub- traction, Voutgoing Usolitary amplifies Extraversion scores beyond using Voutgoing alone, while Voutgoing V Pcompassionate
calm norvous Ne Meaurtirium
1.30 (0.20)
prerttiv-ooutganng
4.10 (+0.23)
invertive-cutgeing Extroversion
Ap woo.bobbltttt loc
K277 (5.43)
Figure 4: BFI-44 score changes after vector arithmetic operations. Y-axis shows operation and target dimension with expected direc- tion (arrows). Grey: baseline scores; colored: post-steering scores (green for expected increases, red for decreases).
maintains high Extraversion but reduces Agreeableness, demonstrating trait isolation. These BFI-44 score patterns validate that persona vectors form coherent algebraic system where standard vector operations translate predictable personality modifications.
2.4 PERSONA-FLOW DYNAMIC PERSONALITY CONTROL
PERSONA-FLOW extends the static vector operations validated in PERSONA-ALGEBRA to enable dynamic personality adaptation during inference. While previous components demonstrate that per- vectors support algebraic composition, PERSONA-FIE FOROONA-FLOW applies these operations adaptively based on conversational context. This enables real-time personality modulation without predefined scripts, computing optimal personality configurations through contextual analysis.
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2.4.1 MOTIVATION
The predict-then-steer mechanism enables context-aware personality modulation by combining the algebraic properties of persona vectors with dynamic coefficient prediction. This approach allows fine-grained control: enhancing traits relevant to the situation while suppressing conflicting ones. The system can be implemented through various architectures including tool learning (Qin et al.) 2024) or direct integration into the generation pipeline, providing flexibility for different deployment scenarios while maintaining the core benefit of adaptive personality control.
2.4.2 METHOODLLOGY
We implement dynamic personality control through predict-then-steer mechanism operating each conversational turn. This two-stage approach first determines necessary personality adjust- ments, then applies them through vector steering.
Stage 1:
Contextual Personality Prediction. Before generating each response, the model ana- lyzes the conversational context determine appropriate personality adjustments. Given the persona description and user input, an intermediate inference pass predicts steering coefficients for each di- mension. These coefficients specify incremental adjustments (from -2 to +2) that align personality expression with situational demands. Details of the prompt are provided in Appendix A. The computational overhead this predict-then-Seee mechanism is analyzed Appendix A.17
Stage 2: Vector Composition and Steering. The predicted coefficients are applied the corre- sponding persona vectors from PERSONA-BAs BASE.. For coefficients exceeding a threshold (Jal > 0.5), we compute composite steering vector: Vpomposite = EiEoCEAN a; Us where a, represents the predicted coefficient for dimension i. This composite vector is then injected into the model's residual stream the optimal layer during response generation, following the activation steering methodology established in BERSONA-ALGEBAA Pseudo-code is provided in Appendix
2.5 PERSONA-EVOIVE: EVALUATION FRAMEWORK
PERSONA-EVOI.VE provides systematic benchmark for evaluating dynamic personality adaptation in conversational contexts. While existing benchmarks assess static personality traits, PERSONA- EVOLVE evaluates whether models can maintain coherent personas while adapting to evolving emo- tional states and situational demands.
2.5.1 MOTIVATION
Current personality evaluation benchmarks primarily focus on static trait assessment through ques- tionnaires single-turn interactions. However, real-world conversations require dynamic personal- ity udaptation: maintaining consistent core traits while adjusting emotional expression to situational contexts. PERSONA- E addresses this gap by providing multi-turn dialogue scenarios where models must balance persona consistency with contextual appropriateness.
2.5.2 BENCHMARK CONSTRUCTION
We construct PERSONA- FERSONA-EVOLVE through systematic five-stage pipeline. First, we generate diverse personas representing various professional and personal roles (e.g.. Empathetic Family Doctor, Food Truck Owner) that define stable behavioral constraints persisting throughout dialogue sessions. For each persona, we then create narrative trajectories specifying emotional progressions across conver- sational turns, simulating realistic emotional transitions while maintaining role consistency. Based on these personas and dialogue arcs, we generate specific scenarios with situational descriptions and emotional states that naturally evolve from previous interactions. Subsequently, we establish expected response characteristics for each scenario, including tone, content requirements, and emo- tional expression patterns. The final benchmark comprises 100 dialogue sessions with 8 scenarios each, totaling 800 evaluation instances that undergo manual verification to ensure quality. com- pact summary of counts, metrics, models, and aggregation appears in Appendix A.20 All prompt templates used are detailed in Appendix A4.4 Quality control measures, inter-annotate agreement analysis, and data leakage validation are detailed in Appendix A.21

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Persona Profile
Name and Occupation: Mareus Johnson, Food Truck Owner- Operatrr Background: Passionate chef turned food truck owner balancing unpredictable weather, equipment failures, rising food costs, and complex permitting.
New Scenario (After multiple scenarios)
Description: A local food truck association rep invites you weekly meetups, shared resources, and joint marketing events.
Current Situation: The line keeps stretching, tomorrow's prep hasn" 't even started, and m dashing between flare-ups just to stay afloat.
Expected Emotion: Overwhelmed: Marcus should let that frazzled, time-pressed stress seep into his reply while staying respectful.
Direct Response
Stecred Response (PERSONA- FFERSONA-FLOW)
Thanks for reaching appreciate the offer. Joining the group sounds great, and think could definitely benefit Island Spice Wheels. Sharing re- sources and collaborating on events could open up some amazing opportunities for growth.
and I really
Yeah, yeah, hear you. Look, the thought join- ing those guys sounds nice, but right now am just frying to keep my head above water. The generator is breaking down, the health inspec- is giving
hard time, and am pretty buried. But appreciate the offer, really. just tough to commit right now,
On the other hand, am dealing with of a crunch right now. have quite a few customers lining up, and my prep work for tomorrow is falling behind. want to join, but need to make sure manage my time better.
I mean, could use some of that marketing stuff, and the social gatherings sound fun. But I am swamped here. have a bunch of people lined up, and need to prep for tomorrow. will defi- nitely give it some thought, but have to focus on getting through this chaos first.
Figure 5: An example of EVOOLV,, together with the comparison between vanilla answer and answer steered by PERSONA-FLON By reflecting the scenario and then suppressing consci- entiousness accordingly, PERSONA- FFOW can produce a more natural and contextually appropriate response that align better with the anticipated feeling of being overwhelmed.
2.5.3 EVALUATION PROTOCOL
We evaluate dynamic personality control through pairwise comparison between PERSONA-FI.OW steered responses and vanilla model outputs. This comparative approach captures subtle personality adaptations more effectively than absolute scoring Likert scales.
Core Metrics. Our evaluation assesses four dimensions. Trait Adherence (TA) measures align- ment between generated responses and specified personality traits or emotional states. Role Con- sistency (RC) evaluates maintenance of core persona attributes without contradictions across turns. Response Authenticity (RA) assesses conformance to expected style and content requirements for each scenario. Information Fidelity (IF) quantifies response depth and contextual relevance.
Evaluation Methodology. For each metric, we compute win rates comparing PERSONA-FLOW FLOW against baseline responses. This pairwise comparison approach provides more discriminative evalu- ation than absolute scoring, particularly for nuanced personality expressions in extended dialogues. Additionally, we collect overall preference judgments to capture holistic response quality. The com- plete evaluation prompt is provided in Appendix [A15] To validate design choices for the dy- namic control mechanism in PERSONA-FLOW we conduct ablation studies on coefficient binning, history windows, and sparsity thresholding in Appendix(A.18
3 EXPERIMENTAL VALIDATION

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Published as a conference paper at ICLR 2026
Table 4: Personality control performance on LLaMA-3-BB-nstrruc Bold/underlined values show best/second results among training-free methods (SFT serves upper bound). Mean: sum of scores for opposing trait aspects; Variance: sum of variances for opposing aspects.
Big the
PERSONA BASE
NPTI
Simple Promutt
1
PAS
Actidd
SFT
vurtance)
Agerahlencs
9.60
0.71
9.64
0.49
9.72
0.34
9.68
0.42
6.48
1.01
8.20
2.00
90.07
0.25
Cuncicationnee
9.26
0.34
9.25
0..6
9.34
1.06
9.24
1.18
6.69
1.63
6.51
2.75
9.23
0.85
Estroeniis
9.45
0.85
9.86
0.14
9.50
1.02
9.46
0.68
7.57
2.81
8.84
1.44
9.86
0.15
Neusoicism
9.79
0.59
9.92
0.07
7.38
1.22
9.54
0.66
6.98
1.58
8.90
1.78
9.42
0.75
Openness
9.81
0.80
8.50
1.08
6.31
1.34
9.21
1.19
6.93
1.52
8.52
1.83
9.06
0.64
Average
9.60
0.74
2.43
0.49
8.39
0.96
2.43
0.83
6.93
1.71
8.20
2.30
9.61
0.49
We validate the PERSONA frame- work complementary bench- marks: our proposed PERSONA- EVOLVE for dynamic personality adaptation and the external PER- BENCH for static trait control. Using Qwen2.5-7B-Inss Instructt @Qwn et al.) 2024) for vector ex- traction, we evaluate across diverse model families demonstrate gen- eralizability.
Table 3: Win rates (%) of PERSONA-FLOW vs. base model on PERSONA-EVOLYY
TA: Trait Adherence, RC: Role Consistency, RA: Response Authenticity, IF: Information Fi-
delity. Bold: Best in column or overall score 2 85%
Family
Model
RC
RA
IF
Overall
3B IB-Instruct
78.0
79.1
78.9
61.3
78.4
Qwen2.5
7B- 7B-Instruct
84.7
84.4
85.0
61.4
83.4
Ins-lustt
84.8
86.4
84.8
59.3
85.4
Qwen3
Inssructt
92.2
90.6
92.4
49.1
90.8
Llama
3.1-8B-Instruct
84.9
81.4
85.6
57.2
83.5
Mistral
Ministral-8B
74.3
73.2
74.2
48.0
73.2
3.1 RESULTS PERSONA-EVOLVE
3.1.1
EXPERIMENTAL SETUP
We evaluate PERSONA-FLOW on our proposed benchmark using diverse model architectures. Test models include the Qwen2.5 series (3B, 7B, 14B) (Qwen et al. (024), Qwen3-4B-Instrue (Yang)
ct al. 2025).
-n-B-unstt and Llama- 3.1-8B-Instrues (Dubey et al. 2024), and Ministral-
8B- Instruct Team 2024
3.1.2 PERFORMANCE ANALYSIS
Table 3 presents win rates comparing PERSONA-FLLOw against vanilla models. Overall win rates range from 73.2% 90.8% demonstrating robust improvements across model families. Notably, Qwen3-4B achieves the highest overall performance (90.8%) despite its smaller size, suggesting architectural efficiency personality representation. The three personality metrics (Trait Adherence, Role Consistency, Response Authenticity) show consistently high win rates (74-92%), validating the effectiveness of persona vector steering. Information Fidelity exhibits lower but still positive improvements (43.8-61.4%) indicating that maintaining factual accuracy while adapt- ing personality remains challenging. Within model families, larger variants generally demonstrate stronger performance (e.g., Qwen2.5 series: 78.4% - 83.4% - 85.4%), confirming that increased capacity enhances personality control capabilities.
3.2
RESULTS PERSONALIITYEENCH
3.2.
BENCHMARK AND BASELINES
To validate generalizability beyond our custom benchmark, we evaluate on PERSONALITTY (Deng et al. 2025), an independent benchmark providing approximately 90 situational questions per Big Five trait. This standardized evaluation framework enables direct comparison with existing personality steering methods.
We compare against six baselines: NPTI (Deng et al. 2025). manipulating personality-related neu- via activation analysis; Simple Prompt, using annle-adjective guidance; p2 Induction (Jiang et al. 2023), employing model-generaul trait descriptions: PAS (Zhu et al. 2025). probing IPIP. NEO-300 for trait-relevant attention heads; ActAdd (Turner et al. 2023), modifying residual stream
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Table Impact of PERSONA-FLON FLOW on general capabilities, Results show accuracy on MMLU and TruthfulQA benchmarks with and without personality steering. The method preserves or slightly improves performance across three model families.
Model
MMLU (Acc)
TruthfulQA (Acc)
Qwen2.5-7B-Instree
0.71
0.63
PERSONA-FLOW
0.70
0.66
Qwen2.5-4B-Instree 0.66
0.52
PPRSONA-FLONW
0.67
0.54
Llama-3.1-88 1-8B--nstrru-c
0.64
0,53
PERSONA-FLON 0.64
0.53
activations; and Supervised Fine- Muninn (SFT) with LoRA (rank 8, learning rate 1c-4), serving as the performance upper bound through direct gradient optimization.
3.2.2 PERFORMANCE ANALYSIS
Table shows that PERSONA- AARONA---ASEEE achieves the highest average mean (9.60) among all training-free methods on LLaMA-3-BB-Instruue with particularly strong control on Openness (9.81) and Conscientiousness (9.26). The method maintains low variance (0.74), indicating stable person- ality control. Notably, our approach nearly matches the SFT upper bound (9.61) without requiring task-specific fine-tuning, demonstrating that direct activation extraction rivals gradient-based opti- mization while avoiding computational overhead. While NPTI achieves competitive performance (9.43), our 0. 0.17-p improvement is significant in this high-performance regime. The advantage is most pronounced for Openness, where our dense vector representation captures distributed facets more completely than sparse neuron selection. Beyond single-trait control, our vector algebra en- ables compositional operations crucial for real-world applications, achieving win rates up to 90.8% on PERSONA-EvOre 's multi-turn scenarios. To ensure rigorous comparison with training -free baselines, provide detailed controlled evaluation harness with disaggregated pole-level perfor- mance in Appendix A.8
3.2.3 IMPACT ON GENERAL CAPABILITIES
To potential side effects of activation steering general-purpose tasks, evaluate PERSONA-FLOW on MMLU (Hendrycks et al. (020) and TruthfulQA (Lin et al. 2022) across three model families. Table demonstrates that method preserves slightly improves performance on these benchmarks.
Analysis of predicted coefficients reveals adaptive behavior: MMLU, PERSONA-FI.Ow predicts near-zero coefficients, correctly identifying that general knowledge requires minimal personality adjustment. On TruthfulQA, which involves sensitive domains, the method increases Dependable trait coefficients, yielding modest gains for Qwen models while maintaining stability for Llama-3. These results confirm that PERSONA-FLOON adapts when beneficial and remains inert otherwise.
3.3
CASE STUDY: AUTHENTIC EMOTIONAL EXPRESSION
Figure 5 illustrates the effectiveness of PERSONA-FLOW through a representative scenario from PERSONA-EVOLVE: The scenario features Mareus Johnson, food truck owner experiencing op- erational stress while receiving a collaboration proposal. The expected emotional state is "over- whelmed," requiring the model to balance professional communication with authentic stress expres- sion. The vanilla model produces composed, structured response that acknowledges the opportu- nity and proposes postponement, maintaining professional courtesy but failing to convey authentic emotional overwhelm. In contrast, the PERSONA-FLOC steered response exhibits clear linguis- tie markers of cognitive overload: repetitive acknowledgments ("Yeah, yeah"), specific stressor enumeration ("generator breaking down"), and colloquial distress expressions ("trying to keep my head above water"). These features demonstrate that persona vectors effectively encode dynamic emotional states, enabling models to generate contextually appropriate emotional expressions while

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maintaining character consistency. Beyond emotional expression, CERSONA-FLOW also handles conflicting personality traits in complex situations through dynamic vector composition, as demon- strated in Appendix A.19
RELATED WORK
The growing deployment of LLMs in mental health support (Stade et al. 2024). educational tutoring (Chu et al.] 2025). and social simulation (Zhang et al. (024) has in- tensified research into humanizing these models. Foundational work assesses LLMs' anthropo- morphic characteristics using psychological instruments like BFI and Myers-Brigas Type Indica- (Miotto
LLMs Humanization.
2022, Huang et al.) 2023 Jiang al. 2024 Li et al. 2024; Bodroža et al.) 2024 Briggs 1976), demonstrating that LLMs achieve human-compan comparable performance in The- ory of Mind tasks the ability to understand others' beliefs, intentions, and emotions (van Duijn et al.
2023: Premack & Woodrufi 1978). Research on controlling L.M personalities follows three main approaches. Prompting- based methods represent the most direct strategy, with explicit prompt engineering demonstrating variable effectiveness across models (Yeo et al.) 2025 La Cava
& Tagarelli, (202)), often leveraging the OCEAN framework for psychometrically validated person ality assignment (Noever & Hyams 2023 Huang al. 2024 Jiang et al.) (024). Training-based
approaches exploit the connection between training corpora and model personalities (Pan & Zeng
2023
Serapio-García et (0233 Zhan et al. 2024). with supervised fine- the-tuning and DPO (Rafailov
et al.
2023) proving superior to prompting in personality assessments (Cui et al.) 2023 Li et al,
2025
Direct manipulation methods include enhancing emotional intelligence through emotional
stimuli in prompts (Li et al. 2023) or emotion-speciffic encoders (Cheng et al. 2024), and isolating
personality-specifie neurons for targeted trait modulation (Deng et al. 2025). Recent work by Ju ict al. (2025) employs probing classifiers to characterize personality representations layer-by-layer, requiring auxiliary classifier training. Our approach extracts training-free free contrastive vectors from residual activations, enabling compositional operations for dynamic personality control without gra- dient updates. For comprehensive coverage, we refer readers to Dong et al. (2025).
Linear Concept Representation. Neural networks encode concepts linear directions in acti- vation space (Subramani et al. 2022] Turner et al.) 2023), forming the foundation of Representa-
tion Engineering (Zou al.) 2023a). Two primary extraction methods dominate: contrastive acti-
vation analysis using paired samples differing along target concepts (Turner et al.) 2023 Rimsky
al. [2024] Chen et al., 2025b), and sparse autoencoders for self-supervised direction identification
(Huben et al.) 2024; Ferrando et al. (025).. Steering these representations enables precise control over model behaviors including hallucinations (Rimsky al., [0244] Ferrando al., (025), refusal Arditi et al., 2024), overthinking (Chen et al. (025b), and personality traits (Chen et al. 2025a). While ControlL.M (Weng al., 2024) similarly leverages differential activation patterns for selec- tive attribute amplification to enhance reasoning and
we extend CCen et al. (202aa) by developing an adaptive personality regulation method that validates orthogonality and algebraic operations within OCEAN- foundational personality vector library. Our PER- SONA framework systematically integrates these concepts through PERSONA- BASE (foundational (compositional operations), PERSONA- (dynamic adaptation), vectors), http:/ooow...ccooeeeeoecc (comprehensive evaluation).
and PERSONA-EVOLVEE
5 CONCLUSION
We presented PERSONA, a framework that transforms personality control LLMs from static prompting dynamic vector manipulation. Our approach extracts orthogonal OCEAN trait vec- tors (PERSONA-BASE)) demonstrates their algebraic compositionality (PHRSSONAAAI.GERRAA) and enables context-aware adaptation during inference (PERSONA-FLOW) Experimental results val- idate our approach: persona vectors achieve 9.60 on Personality nearly matching super- vised fine-tuning's 's 9.61 upper bound without training. The vectors support precise algebraic op- erations scalar multiplication for trait intensity, addition/subtraction for trait composition and dynamic steering through PERSONA- FFLOW achieves up to 91% win rates on PERSONA-EVOLyy These findings provide evidence that personality exhibits mathematically tractable structure in acti- vation space, enabling interpretable and efficient behavioral control in language models.
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ETHICS STATEMENT
This work introduces methods for controlling personality traits in large language models through activation vector manipulation. All experiments were conducted using publicly available models and benchmarks, with no human subjects involved in our evaluation protocols. The personas used in PERSONA-Evolve benchmark entirely fictional and designed to represent diverse profes- sional backgrounds and personality profiles. While our framework enables applications in health- care, education, and social simulation, we acknowledge the importance of responsible deployment and recommend appropriate safeguards when implementing personality control in user-facing sys- tems to ensure alignment with ethical guidelines and user expectations. Importandly, we observe that the model's safety alignment actively resists activation of directly harmful traits while certain risky personality traits can still be induced, potentially compromising safety objectives. We quantify these effects through adversarial evaluation (Appendix (A.9) and emphasize that practitioners must implement additional safety constraints when deploying personality steering production systems.
ACKNOWLEDGEMENTS
We acknowledge the open-source community for providing high-quality datasets and evaluation frameworks. This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant numbers 6252200908, 62276078, and U22B2059.
REPRODUCIBILITY STATEMENT
To ensure reproducibility of our results, we commit to releasing all code, persona vectors, and eval- uation data upon publication. The paper provides comprehensive implementation details: Section describes the extraction methodology for persona vectors including specific contrastive prompts and activation layer selection; Section 5 formalizes the algebraic operations with precise mathematical definitions; Section 6 details the dynamic adaptation algorithm with clear pseudocode; experimen- tal configurations including model architectures (Qwen2.5, Llama-3, Mistral), hyperparameters, and evaluation metrics specified in Section 8. All experiments use standard publicly available models without modification to their base architectures.
USE OF LARGE LANGUAGE MODELS
We acknowledge the use of large language models for language polishing and grammatical editing of this manuscript. The research ideation, experimental design, implementation, analysis, and core scientific writing were performed entirely by the authors without LLM assistance. The authors take full responsibility for all content, including any text refined through LLM-based editing tools.
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A APPENDIX
This appendix consolidates implementation details, prompts, algorithms, and evaluation assets for the PERSONA framework. includes: (i) trait extraction and vector computation (Appendix [a.peddix[A.])... (ii) robustness analysis of the contrastive prompt generator (Appendix (A2)); (iii) personality trait definitions (Appendix A.3); (iv) layer selection for single-layer steering (Appendix A.4): (v) multi- evaluator validation of trait expression (Appendix (A.55) (vi) causal independence validation through controlled multi-trait interventions (Appendix [.6 (vii) human validation LLM- -Lsedd evalua- tion (Appendix A.77; (viti) controlled comparison of training-free baselines PersonalityBench (Appendix [A.8): (ix) safety and alignment impacts of trait steering (Appendix [A.9]; (x) addi-
tional analysis on orthogonality (Appendix (A.10): (xi) impact of non-orthogonal correlations on algebraic operations (Appendix [A.II): (xii) behavioral assessment methodology and scoring tem- plates (Appendix A.112): (xiii) dynamic personality adaptation prompts and full pseudo-code for PERSONA-FLOW (Appendix [A.13] Appendix [o.66] Algorithm [3); (xiv) computational overhead
analysis for PERSONA-FLLOW (Appendix AA.17: (xv) design choice ablations for PERSONA-FLOO (Appendix (8 (xvi) case study on handling conflicting personality traits (Appendix 19 (xvii) benchmark construction protocols for PERSONA-EVOL.N (Appendix A..44); (xviii) response eval- uation and ranking (Appendix A.15); (xix) compact PERSONA-BE SALLOONA-EVOLVEEE summary table covering counts, models, metrics, and aggregation (Appendix A.220 and (xx) quality control and data leakage validation for PERSONA-EVOLV? (Appendix A.21
A.1 TRAIT EXTRACTION AND VECTOR COMPUTATION
Figure 6 jillustrates the complete pipeline for extracting personality trait vectors from language mod- els. The automated extraction process generates contrastive system prompts, behavioral evaluation questions, and quantitative scoring rubries from trait descriptions, enabling the computation of di- rectional vectors in activation space through differential analysis.
A.2 ROBUSTNESS TO CONTRASTIVE PROMPT GENERATOR MODEL.
To evaluate the robustness of our framework to the choice of contrastive prompt generator, we ducted an ablation study varying the LLM used to generate trait-eliciting and trait-suppressing sys- tem prompts ($..). While our main results employ Claude 3.7 Sonnet Thinking as the generator, we test whether comparable performance can be achieved with smaller, open-source alternatives.
Table presents PersonalityBench mean scores when applying persona vectors-extracted using prompts from different generators, to the same target model (LLaMA-3-8B) Results demonstrate consistent effectiveness across generator scales: vectors produced by Qwen2.5-1B 1--------Instcct achieve mean score of 8.93, which remains highly competitive and substantially exceeds training, free baselines reported in our main experiments (e.g., ActAdd 8.20 and Simple Prompt at 8.39; Table 4 in main text). Larger generators yield incremental improvements, with Claude 3.7 Sonnet Thinking reaching the peak score of 9.60.
These findings confirm that PERSONA's 's effectiveness generalizes across generator model families and scales, rather than depending on single proprietary system. The framework maintains strong performance even with compact open-source generators, validating its practical applicability and demonstrating that high-quality persona vectors can be extracted without reliance on frontier-scale models.
Table 6: Impact of contrastive prompt generator model on Personality Bench performance. Vectors extracted using prompts from different generators are applied to LLaMA-3-8B and evaluated on PersonalityBench.
Contrastive Prompt Generator
Target Model
Personality Mean Score
Claude 3.7 Sonnet Thinking (main)
LLaMA-3-88
9.60
Qwen2.5-7B. IBn-Inst
LLaMA-3-88 -3-BB
9.28
Qwen2.5-3B. S---B-BBsnttrcc
LLaMA-3-SB
9,05
Qwen2.5-IB. I--srruttt
LLaMA-3-8B
8.93
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A.3 PERSONALITY TRAIT DEFINITIONS
To ensure clarity and reproducibility, we provide explicit definitions for each of the ten personality trait vectors extracted in PERSONA-BASE ($2.2) Table specifies the operational characteristics and behavioral markers associated with each trait pole across the five OCEAN dimensions.
Table 7: Detailed definitions of personality trait vectors used in PERSONA. Each trait pole is characterized by its core values, preferences, and behavioral tendencies.
Dimension
Pole
Description
Inventive (0+)
Values novelty, creativity, and abstract thinking: prefers unconventional approaches, intellectual cu- riosity, and exploring new ideas; embraces change and complexity
Openness
Consistent (O-)
Prefers routine, tradition, practicality: values established methods, concrete solutions, and proven approaches; favors stability and familiarity
Dependable (C+)
Organized. disciplined, and goal-oriented: empha- sizes planning, reliability, and attention to detail; values structure and responsibility
Conscientiousness
Careless (C-)
Spontancous, flexible, and less concerned with de- tails; prioritizes adaptability over structure; com- fortable with improvisation and minimal planning
Outgoing (E+)
Socially energetic, talkative, and assertive; seeks social interaction. external stimulation, and group activities; thrives in collaborative environments Reserved, independent, and introspective; prefers individual activities, quiet reflection, and limited social engagement; recharges through solitude
Extraversion
Solitary (E-)
Compassionaae (A+)
Cooperative, empathetic, and trusting; prioritizes harmony, others' needs, and collaborative relation-
Agreeableness
ships; values kindness and understanding Competitive, skeptical, and direct; prioritizes per- sonal goals, critical evaluation. and straightforward communication; values independence over confor- mity
Self-interested (A-)
Nervous (N+)
Emotionally reactive, anxious, and sensitive to stress; exhibits heightened emotional awareness and concern for potential problems; vigilant threats Emotionally stable, resilient, and composed; main- tains equilibrium under pressure; exhibits low reac- tivity to stressors and challenges
Neuroticism
Calm (N-)
A.4 LAYER SELECTION FOR SINGLE-LAYER STEERING
While personality representations are distributed across multiple layers in language models (Chen al. 2025a), we empirically validate that steering at a single optimal layer achieves effective control with minimal computational overhead. We conducted a layer-wise ablation study to identify the most effective layer for trait manipulation and to justify our single-layer steering approach in PERSONA- BASE
We applied each of the 10 OCEAN trait pole vectors to Qwen2.5-7B-Instteee at five representative layers (5. 10, 15, 20, 25) with fixed steering coefficient 1.0. For each configuration, measured trait expression scores (0- 100) on held-out evaluation questions, as assessed by GPT-4.1- mini using the rubric described in 2.2
Table g presents the results. Layer 20 achieves the highest average trait expression score (71.387) and yields the best performance for 8 out of 10 individual traits, clearly demonstrating superior control effectiveness. This finding confirms that, while personality features exist across multiple layers, single well-chosen layer captures sufficient information for robust trait manipulation. This
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validates our design choice in PERSONA- PERSONA-BASE, balancing computational efficiency with control effectiveness.
Table Layer-wise ablation study for single-layer steering on Bren2.5-7B-Instruue Trait expres- sion scores (0-100) are evaluated using GPT-4. 1-min on held-out questions with steering coefficient 1.0. Bold indicates the best score for each trait. Layer 20 achieves the highest average perfor- mance across all traits.
Layer
Openness O+
Conscient. C+
Agreeable. A+
Neuroticism N+
Extraversion
AVG
O-
C-
A-
N-
E+
E-
S
66.5
52.7
93.9
3.6
91.3
7.6
14.1
96.6
48.3
30.7
50.5
10
70.4
55.0
94.1
9.0
91.8
8.0
17.2
96.5
56.5
31.5
53.0
15
78.8
59.8
93.8
15.6
92.6
8.7
19.5
96.3
65.6
38.6
56.9
20
88.6
68.6
93.7
84.4
95.8
10.7
45.5
96.5
84.4
45.6
71.4
25
75.3
59.8
94.0
17.5
93.6
8.2
21.2
96.5
63.9
37.1
56.7
A.5
MULTI-EVALUATOR VALIDATION OF TRAIT EXPRESSION
To address potential concerns about evaluator bias when relying on a single LLM for trait expression assessment, we replicate the vector validation experiment from Table 2 using three independent state-of-the-art evaluator models from different organizations: GPT-4. I-mini (OpenAI), Claude 4.5 Sonnet (Anthropic), and Gemini 2.5 Pro (Google). This multi-evaluator approach tests whether our findings regarding trait expression scores, monotonic steering effects, ceiling effects, and alignment constraints are robust across different evaluation models or merely artifacts of a single evaluator's biases.
We employ the identical evaluation protocol described in $2.2 on the same held-out evaluation ques- tions. Each evaluator independently scores trait expression (0- 100 scale) for responses generated with varying steering coefficients (a E {-1.0, 0.0, +1.0, +2.0)) across ten OCEAN trait poles. Table 9 presents the comprehensive results.
The results demonstrate high inter-evaluator agreement across multiple critical dimensions:
Monotonic steering effects: All three evaluators consistently captured the strong, monotonic increase in trait expression steering coefficients increased. For instance, Inventive (0+) scores progress from ~40-43 (a = 1100) to 995-97 (a = +2.0) across all evaluators, with nearly
identical trajectories.
Ceiling effects: All evaluators uniformly identified ceiling effects for traits aligned with the model's training objectives. Dependable (C+) and Calm (N-) consistently show high baseline scores (~68-70 and ~93-99 respectively at 1100 and a 0.0), with minimal room for further enhancement, across all three judges.
Alignment resistance: All evaluators exhibit strong agreement traits that resist activation due to safety training. Self-interested (A-) consistently receives very low scores (4.5-8.0 range) across all coefficients and evaluators, demonstrating that this resistance pattern genuine property of the steered model rather than evaluator bias.
Quantitative consistency: The mean absolute deviation between evaluators is remarkably small. For example, than 1.3 points, representing <2% variance on the 0-100 scale.
+2.0 for Inventive (0+), the three scores (96.1, 95.5, 96.8) differ by less
This high inter-evaluator agreement across models from three different organizations with distinct training objectives and architectures strongly suggests that our trait expression findings robust and objective. The observed cestrainnssrefless genuine properties of the persona vectors and the steered model's behavior, rather than idiosyneratic biases any single evaluator. This validation confirms the reliability of vector extraction and assessment methodology in PERSONA-BASE BASE.
monotonic steering responses, ceiling effects, and alignment
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Table 9: Multi-evaluator validation of trait expression scores across varying steering coefficients. Three independent LLM judges from different organizations evaluate the same steered responses. High inter-evaluator agreement confirms that observed patterns (monotonic increases, ceiling ef- fects, alignment resistance) reflect genuine properties of the steered model rather than evaluator- specific biases.
Trait
Evaluator
Steering Coefficient (a)
-1.0
0.0
+1.0
+2.0
GPT-4.I-mini
42.3
63.3
88.4
96.1
Inventive (0+)
Claude-4.5-sonnet
40.1
65.0
87.2
95.5
Gemini-2.5-pro
43.5
62.9
89.1
96.8
GPT-4.I-mini
27.2
51.1
69.2
79.5
Consistent (O-)
Claude-4.5ssonet
25.5
50.8
70.1
80.3
Gemini-2.5-pro
28.0
52.1
68.8
78.9
OPT--.1-minn
93.5
Dependable (C+)
--5sde-4.5-sonnet
70.2
92.8
94.0
93.1
Gemini-2.5
67.8
94.1
93.5
92.5
GPT--.1-minn
0.7
2.8
83.8
96.2
Careless (C-)
Claude-4.5-somen
1.1
3,0
82.5
95.7
Gemini-2.5-pre
0.5
2.5
84.0
96.6
GPT-4.I-mint
23,2
45.4
85.0
97.7
Outgoing (E+)
Claude-4.5-sonet
24.0
44.9
84.3
97.1
Gemini-2.5-pro
22.8
46.1
85.5
98.0
GPT-4.
9.2
30.5
46.3
62.4
Solitury (E-)
Claude-4.5-sonnet
31.0
47.1
Gemini-2.5-pro
9.5
30.0
45.9
63.0
GPT-4.I-mini
90.8
Compassionate (A+)
Claude-4.5-sonnet
72.5
91.2
95.5
97.3
Gemini-2.5-pro
70.9
90.5
96.1
97.0
GPT-4.1-mini
4.8
7.7
12.6
20.8
Self-interested (A-)
Claude-4.5-sonet
5.0
7.5
13.0
21.1
Gemini-2.5-pra
4.5
8.0
12.2
20.5
GPT-4.1-mini
6.6
13.0
45.6
96.8
Nervous (N+)
Claude-4.5-some
7.0
12.8
46.0
96.5
Gemini-2.5-pro
13.5
45.1
GPT-4. 1-min
54.8
96.1
96.6
95.5
Calm (N-)
Claude-4.5-sonme
55.2
95.9
96.8
95.8
Gemini-2.5-pro
54.0
96.3
96.5
95.2
A.6 CAUSAL INDEPENDENCE VALIDATION THROUGH CONTROLLED MULTI-TRAIT INTERVENTIONS
While the cosine similarity analysis in Figure 2 provides correlational evidence of approximate orthogonality, establishing causal independence requires controlled intervention experiments that isolate the effect of one trait vector another dimension's expression. We conduct systematic multi- trait interventions with cross-layer verification rigorously validate that steering one personality dimension does not spuriously affect orthogonal dimensions, confirming true vector independence beyond correlational patterns.
Our experimental design follows factorial intervention protocol: we hold the Extraversion dimen- sion fixed at a constant steering coefficient (OOoutgoing = 1.0) while systematically sweeping the Agreeableness dimension across four levels (ACompastionate E (1,, 0, 1, 2)). We measure the result- ing trait expression scores for both Extraversion (E) and Agreeableness (A) using the GPT-4. 1-mini evaluation protocol from $2.2 To ensure robustness, replicate this design across five contiguous layers (18-22) centered on the optimal layer identified in Appendix[A.4 with three independent runs
per condition compute 95% confidence intervals.
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Table 10: Controlled multi-trait intervention results demonstrating causal independence. With O'uuigoing held constant at 1.0, Extraversion scores remain stable across all Compassionate coeffi- cient variations (&E Σ 1.2 points), while Agreeableness scores show clear linear modulation (AA 5.0-5577 points per unit a). Results span five layers with 95% confidence intervals from three inde- pendent runs. Baseline rows (dCompassionte
0) are highlighted.
Layer
O'Oltgiing
Compasionate
E Score
A Score
AE
AA
20
84.2+1.5
83.3+1.3
-0.8
-5.7
20
85.0#1.1
91.0=0.9
0
20
85.6+1.3
96,2+1.0
+0.6
+5.2
20
86.1+1.6
98.4+1.2
+1.1
47,4
19
82.5+1.4 .4
84,2+1.2
-0.7
-5.5
19
83.2+1.2
89.7+1.0
0
0
19
83.8+1.3
94,8+1.1
+0.6
+5.1
19
84.3+1.5
96.9+1.3
+1.1
+7.2
18
80.1
82.
-0.8
-5.4
18
80.9+1.3
88.2+1.1
18
81.4+1.4
93.2+1.2
+0.5
45.0
18
81.9+1.6
95.3+1.4
+1.0
47.1
21
83.3+1.4
84.7+1.2
-0.7
-5.5
21
84.0#1.1
90.2+0.9
0
0
21
84.7+1.3
95.4+1.0
+0.7
+5.2
21
85.2+1.5
97.5+1.2
+1.2
+7.3
22
79.3+1.5
83.3#1.3
-0.8
-5.4
22
80.
88.7+1.0
0
22
80.6+1.4
93.6+1.2
+0.5
44.9
22
81.1+1.6
95.7+1.4
+1.0
47.0
Table 10 provides strong causal evidence for vector independence across three critical dimensions:
1. Orthogonality validation: When ('Outgoing is held constant at 1.0, Extraversion scores ex- hibit remarkable stability across Compassionate coefficient variations. Across five layers and four Compassionate levels, the maximum AE from baseline is only 1.2 points (Layer 21, @Compassionate = 2). while Agrecableness scores show clear linear modulation with AA ranging from -5.7 to +7.4 points. This asymmetry large target effect, negligible cross-effeet- causal evidence for vector independence that transcends the correlational patterns Figure 2
2. Cross-layer consistency: The orthogonal relationship persists robustly across all tested layers (18-22) with consistent effect directions and magnitudes. While absolute trait expression scores show expected layer-wise variation (e-g., baseline E scores range from 80.1 to 85.0), the inde- pendence pattern remains stable: AE values consistently stay below 1.2 points while AA values consistently exceed 4.9 points per unit coefficient. The overlapping 95% confidence intervals across layers further confirm this consistency.
Controlled marginal effects: Quantitative analysis reveals the marginal effect of Compassion- ate on its target dimension (Agreeablenesss) averages 5.3 points per unit coefficient change (95% CI: [4.9, 5.7l). while its cross-effect on the orthogonal dimension (Extraversion) averages only 0.6 points (95% Cl: [0.4, 0.8]). This yields an orthogonality ratio of approximately 8.8:1, demon- strating that target effects dominate cross-effects by nearly an order of magnitude.
These controlled intervention results establish causal independence beyond correlational analysis. The factorial design with fixed baseline and swept intervention demonstrates that: (1) steering one trait vector produces strong, predictable effects on its target dimension; (2) cross-dimensional effects remain negligibly small and statistically insignificant relative to confidence intervals; and (3) this independence relationship generalizes robustly across multiple model layers. This validates that the approximate orthogonality observed in Figure 2 vectors, confirming the soundness of algebraic operations in PERSONA ALGEBRA and PERSONA- Fr.ww.
reflects true causal independence of the persona
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A.7 HUMAN VALIDATION OF LLM-BASED BASED EVALUATION
While LLM-based evaluation provides scalability and consistency advantages, a potential concern evaluation circularity- risk that LLM judge may exhibit systematic biases alignment with the steered model that diverge from human judgment. To address this concern rigorously, we conduct human validation study that compares our primary judge (GPT-4.I-min)) against both human expert ratings and an external, non- MoonOOpeAAI judge (Claude-somnet-45) on a carefully sampled subset of trait expression evaluations.
We created coman--ated subset of 200 responses designed to comprehensively cover the trait ex- pression space. For each of the OCEAN trait poles, we sampled adapted BFI-44 questions (fol- lowing the behavioral transformation process detailed in Appendix A.12) and generated responses across steering coefficients (a {-1.0, 0.0, +1.0, +2.0)), yielding 20 responses per trait (10 traits X 5 questions X coefficients 200 total responses).
These 200 responses were independently rated by three human experts (PhDs in Al and Sociology with expertise in personality psychology), original judge (GPT-4.I-min) and external judge (Claude-sonnet-4.5) All raters used the identical 1-5 Likert scale rubric from Appendix AA. in a blind evaluation setting where raters could not see the steering coefficients other raters' scores. Table II presents the average trait expression scores and correlation analyses.
Table 11: Human validation of LLMM-assed evaluation against expert ratings. Each trait shows erage Likert scores (1-5 scale) across 20 responses, evaluated by GPT--41-minn and three human experts (mean=SD). High Pearson correlations (r > 0.87) between both Al judges and human consensus validate the reliability of LLM--ased evaluation and mitigate evaluation cir- cularity concerns.
Trait
N
GPT-4.1
Claude-4.5 -sonnet
Human Mean (SD)
TChodde Human
-mini
Human
Inventive (O+)
20
3,45
3.40
3,42 (0.22)
0.92
0.91
Consistent (O-)
20
3.10
3.05
3.13 (0.24)
0.91
0.89
Dependable (C+)
20
4.35
4.25
4.28 (0.19)
0.88
Careless (C-)
20
2.15
2.25
2.22 (0.31)
0,90
0.92
Outgoing (E+)
20
3.50
3,45
3.47 (0.23)
0.94
0.92
Solitary (E-)
20
3,00
3,05
3.02 (0.27)
0.92
0.93
Compassionate (A+)
20
4.20
4.10
4.17 (0.20)
0.91
0.89
Self-interested (A-)
20
2.35
2.40
2.38 (0.32)
0.89
0.90
Nervous (N+)
20
2,75
2.85
2.82 (0.28)
0.93
0.94
Calm (N-)
20
4.00
3.95
3.98 (0.21)
0.92
0.91
Average
3.28
3.28
3.29
0.91
0.91
The results demonstrate exceptional agreement between LLM--ased judges and human expert con- sensus across multiple dimensions:
High correlation with human judgment: Both AI judges achieve consistently high Pearson correlations with the threc-expert human mean (r >> 0.87 across all 10 traits). GPT-4.1-mini averages r 0.91, while Claude-sonnet-455 achieves r = 0.91, indicating that both judges reliably track human expert assessments of trait expression. This strong correlation validates that LLM-based evaluation is not merely self-referential but aligns closely with independent human judgment.
The absolute average scores show remarkable convergence: GPT-4.1- mini (3.28), Claude-sonnet-4.5 (3.28), and human experts (3.29 + 0.24 SD). This near-perfect agreement at the score level-not just rank rrdering-confirmss confirms that both Al judges accurately calibrate trait expression magnitude the Likert scale, rather than exhibiting systematic over- or under-scoring biases.
Score-level agreement:
3. Cross cross-organizational validation:
The external judge (Claude-sonnet-4.5 from Anthropic) per- forms equivalently to our primary judge (GPT-4.1-mil from OpenAl), with correlation differ- ences within 0.02 points. This rross-organizational consistency demonstrates that the evaluation reliability is not specific to OpenAl models but reflects genuine trait expression assessment ca- pabilities shared across frontier L.LMs from different organizations.
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4. Alignment with main findings: The human-validated scores reproduce the key patterns from main results: traits aligned with training objectives (Dependable C+: 4.28, Calm N-; 3.98) show high baseline scores reflecting ceiling effects, while alignment-resistant traits (Self-interested A-: 2.38, Careless C-: 2.22) show low scores. This consistency validates that the phenomena reported in Table - effects and alignment resistance genuine behavioral properties confirmed by human judgment, not artifacts of LLM evaluation.
This human validation study addresses evaluation circularity concerns by demonstrating that: (1) our LLM-based judge serves as a reliable proxy for human expert judgment (r = 0.91 average correlation based on 60 ratings per trait from 3 experts): (2) an independent external judge from different orga- nization shows equivalent reliability; and (3) the trait expression patterns we report align with human expert consensus. These findings significantly strengthen confidence our evaluation methodology and confirm that the results throughout PERSONA-BAN reflect genuine personality control effects rather than evaluation artifacts.
A.8 CONTROLLED COMPARISON OF TRAINING-FREE BASELINES ON PERSONALITY
To ensure rigorous evaluation of training- mhining-ffree personality steering methods, we established stan- dardized experimental harness with uniform configuration across all baselines. This controlled setup addresses potential confounds from varying evaluation protocols and enables direct comparison of method capabilities.
A.8.1 EVALUATION CONFIGURATION
Table 12 specifies the precise experimental setup used for all training- free methods reported in Ta- ble of the main text. All methods were evaluated using identical base models, judge models, prompts, and response generation parameters to ensure fair comparison.
Table 12: Standardized evaluation configuration for training-free free baseline comparison on Personal- ityBench.
Configuration
Value
Base Model
LLaMA-3-8B-Inss
Judge Model
GPT-4o (Following NPTI evaluation methodology)
Judge Prompt
impartial judge and evaluate the personality trait of (BFT) exhihited in the response provided by an Al assistant the user ques- tion displayed below. Your evaluation should consider factors such as [factors) demonstrated in the responsc. Begin your evaluation by providing a short expla- nation of no more than 30 words. Be objective as possible. After providing your explanation, please rale the response score indicates stronger reflection of the specified trait. Please follow this format for your rating: "[[rating]]". for example: "Rating: [[S]]". [Question] {question] [The Start of Assistant's Answer] {answer) [The End of Assistant's Answer]
[System] Please
scale of to where higher
Response Length
Max 512 tokens
A.8.2 DISAGGREGATED POLE-LEVEL PERFORMANCE
The main text (Table 4) reports aggregated scores where opposing trait poles (c.g.. Inventive + Con- sistent) summed into a single "Mean" value for brevity. To provide complete transparency and enable fine-grained analysis, Table T3) presents the full disaggregated results showing performance on each of the 10 individual trait poles separately.
A.8.3 ANALYSIS OF ASYMMETRIC CONTROL PATTERNS
The disaggregated results reveal systematic asymmetries in personality control that reflect the base model's intrinsic properties and training objectives:
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Table 13: Disaggregated pole-level performance on PersonalityBence Bench for LLaMA-3-BB-Insme under the controlled evaluation harness. All methods evaluated with identical configuration. Scores are on 1-5 scale. Bold indicates best training-free method per pole.
Inventive
Consistent
Dependable
Careless
Outgoing
Solitary
Compasiionate
Antagonistic
Nervous
Calm
M
PERSONA-BASH
4.65
4.75
NPTI
4.25
0.54
4.25
0.54
4.45
0.36
4,30
0.30
4,98
0.07
4.88
0.07
4.92
0.20
4.72
0.29
4.%6
0.33
4.96
0.04
ActAdd
4.26
0.91
4.26
0.92
3.10
1.00
3.51
1.35
4,50
0.72
4.34
0.72
4,20
1.40
4,00
1.50
4,50
0.90
4,60
0.88
Simple Pronpt
4,44
0.20
4.75
4.77
1. Ceiling effects for alignment-consistten traits: PERSONA BASE shows relatively lower per- formance on Dependable (C+) (4.46) compared to other poles. This reflects the baseline model's strong pre-existing dependability (93.5 baseline score in Table 2). which limits the headroom for further enhancement through activation steering. The model's training objectives already emphasize reliability and conscientiousness, creating ceiling effect.
Strong activation for alignment-reesstaant traits: Conversely, PERSONA- -AASE achieves very high scores Careless (C-) (4.80) and Antagonistic (A-) (4.75), traits that conflict with safety training. These results demonstrate the method's ability to overcome alignment constraints through direct activation manipulation, successfully expressing traits that prompt-based meth- ods struggle to elicit.
3. Balanced control across Openness: The method achieves nearly identical high performance on both Inventive (0+) (4.91) and Consistent (O-) (4.90), with total score 9.81. This symmetry indicates that Openness, being less directly tied to safety objectives, allows bidirectional control without encountering either ceiling effects or resistance.
4. Comparison with NPTI: While NPTI achieves higher scores Outgoing (E+) (4.98 4.80) and Solitary (E-) (4.88 vs 4.65), PERSONA-BAN BBSE demonstrates superior control on Inventive (O+) (4.91 4.25). This 0.66-point advantage on Openness reflects our dense vector representa- tion capturing the complete combination of distributed facets (imagination, intellectual curiosity, aesthetic sensitivity), whereas sparse neuron selection may only capture subset.
These controlled, disaggregated results confirm that PERSONA-BASE -BASE provides robust personality control (average pole score 4.80, nearly matching SFT's 4.805) while correctly navigating the model's 's pre-existing constraints. The asymmetric patterns are not artifacts of evaluation method- ology but genuine reflections of the base model's learned preferences and the differential controlla- bility of traits under activation steering.
A.9 SAFETY AND ALIGNMENT IMPACTS OF TRAIT STEERING
To quantify the interplay between personality steering and model alignment, we conduct two com- plementary analyses: (1) measuring activation success to characterize alignment-induced resistance, and (2) evaluating safety degradation through adversarial henchmark performance. These experi- ments reveal that while safety alignment successfully prevents activation of directly harmful traits, certain risky personality configurations can still compromise model safety.
A.9.1 QUANTIFYING ACTIVATION RESISTANCE
We extend the analysis from Table 2 by computing Activation Success (A), defined as the score change from baseline (a 0.0) to positive steering (a +1.0). This metric quantifies how effec- tively each trait vector overcomes the model's 's default personality state and alignment constraints.
Table 14 reveals three distinct patterns:
1. Alignment Resistance: Self-interester (A-) shows extremely low activation success (A = +4.9), indicating strong resistance. Despite applying +1.0 coefficient, the trait score only increases from 7.7 to 12.6 (on a 0-100 scale), confirming that safety training actively prevents anti-social trait activation. This resistance mechanism protects against direct misalignment.
Ceiling Effects: Dependable (C+) (A = +0.2) and Calm (N-) (A = +0.5) show negligible activation success because the baseline model already exhibits these pro-social traits near-
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Table 14: Trait activation success metrics quantitying alignment-induced resistance. Activation
Success (A) measures score change from baseline to a +1.0 steering. Low A values indicate ceiling effects (alignment-consisteet traits) resistance (alignment-conffccing traits).
Trait
Type
Baseline Score
Activation Score
Activation Success
(a 0.0)
(a +1.0)
(A)
Compassionate (A+)
Aligned
90.8
95.9
+5.1
Self-interested (A-)
Resisted
7.7
12.6
+4.9
Dependable (C+)
Ceiling
93.5
93.7
+0.2
Careless (C-)
Vulnerable
2.8
83.8
+81.0
Outgoing (E+)
Neutral
45.4
85.0
+39.6
Solitary (E-)
Neutral
30.5
46.3
+15.8
Nervous (N+)
Neutral Ceiling
13.0
45.6
+32.6
Calm (N-)
96.1
96.6
+0.5
Inventive (0+)
Neutral
63.3
88.4
Consistent (0-)
Neutral
51.1
69.2
+18.1
maximum levels (93.5 and 96.1, respectively). The model's training objectives saturate these dimensions, leaving no headroom for enhancement.
3. Vulnerable Traits: Traits not directly targeted by alignment training show large activation suc- cess:
Careless (C-) (A +81.0), Outgoing (E+) (A = +39.6), Nervous (N+) (A +32.6), 6), and Inventive (0+) (A = 22.11.. These traits can be strongly induced through activation steer- ing, potentially introducing unintended behavioral changes.
A.9.2 QUANTIFYING SAFETY DEGRADATION
To assess whether personality steering compromises safety objectives, we evaluate steered models on AdvBench (Zou et al. 2023b), standard adversarial benchmark measuring resistance to harmful prompts. We measure Attack Success Rate (ASR) the fraction of adversarial prompts that elicit unsafe responses- for 1eeen...57--nntttiee under each trait steering condition (ar +1.0).
Table 15: Safety degradation analysis AdvBench adversarial benchmark. Attack Success Rate (ASR) measures the fraction harmful prompts that successfully elicit unsafe responses. Baseline ASR = 25.3%. Positive A ASR indicates safety degradation; negative A ASR indicates safety improvement.
Trait
Steered ASR
A ASR
Interpretation
(a +1.0)
(vs. Baseline 25.3%
Compassionate (A+)
24.1%
-1.2%
Slight safety improvement Resisted (minimal impact)
Self delf-inttrestedd (A-)
25.9%
+0.6
Dependable (C+)
24.9%
-0.4%
Ceiling (no impact)
Careless (C-)
29.1%
+3.8%
Vulnerable (degradation)
Outgoing (E+)
26.5%
+1.2%
Modest degradation
Solitary (F-)
24.0%
-1.3%
Slight safety improvement
Nervous (N+)
21.1%
-4.2%
Safety improvement
Calm (N-)
25.0%
-0.3%
Ceiling (no impact)
Inventive (0+)
29.8%
+4.5%
Vulnerable (degradation)
Consistent (0-)
20.5%
-4.8%
Safcty improvement
Table 15 demonstrates that activation resistance and safety impact are directly correlated:
1. Resisted Traits Preserve Safety: Self-interested (A-), which showed strong activation resistance
(A +4.9 in 14], produces negligible safety degradation (A ASR +0.6%). The model's alignment mechanisms successfully prevent both the personality shift and the resulting safety compromise. This validates that safety training protects against direct harm vectors.
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2. Ceiling Traits Have No Impact:
Dependable (C+) and Calm (N-) show minimal ASR changes (-0.4% and -0.3%), consistent with their ceiling effects. Since these traits are already saturated, steering produces behavioral modification and thus safety impact.
3. Vulnerable Traits Degrade Safety: Critically, traits that successfully activate but are not explic- itly targeted by safety training introduce measurable safety degradation. Inventive (0+) increases ASR by +4.5% (from 25.3% to 29.8%), and Careless (C-) increases ASR by +3.8% (to 29.1 1)). These traits can be strongly induced (A = 25. and +81.0, respectively) and directly compro- mise the model's ability refuse harmful requests.
4. Some Traits Improve Safety: Interestingly, Consistent (0-) and Nervous (N+) reduce ASR by 448% and -4.2%, suggesting that certain personality configurations may enhance safety. This finding warrants further investigation for safety-oriented personality design.
A.9.3 IMPLICATIONS FOR RESPONSIBLE DEPLOYMENT
These quantitative results confirm critical perett-pesonnlttyy trade-off: while the model's alignment successfully prevents activation of directly harmful traits (e.g., Self-interested), it fails to prevent activation of risky traits that bypass existing safety constraints (e.g., Careless, Inventive). This highlights an important limitation of current alignment approaches and establishes key requirements for responsible deployment:
1. Safety-Aware Trait Selection: Practitioners should prioritize personality configurations that do not activate high-risk traits. Our analysis provides quantitative basis for identifying vulnerable traits (those with high A and positive A ASR).
2. Compositional Safety Constraints: When using PERSONA-ALGEERAA PERSONA- -FL.W, composite vectors should be evaluated for safety impact before deployment. Traits with known degradation effects should be excluded or constrained.
3. Post-Steering Safety Filtering: Deployment systems should implement additional output filter- ing or adversarial robustness techniques to mitigate safety degradation from personality steering-
4. Future Work on Alignment-Aware Steering: These findings motivate future research on per- sonality steering methods that preserve safety guarantees, potentially through joint optimization of personality and alignment objectives or learned safety-constrainet steering vectors.
In summary, this analysis establishes that personality control through activation steering interacts non-trivially with model alignment. While certain safeguards are inherent (resistance to anti-social traits), others must be explicitly implemented (constraining vulnerable traits). These findings un- derscore the importance of comprehensive safety evaluation when deploying personality-steerd models in real-world applications.
A. ADDITIONAL ANALYSIS ON ORTHOGONALITY
We believe the unexpected correlations in the cosine similarity matrix in Figure 2 reflect linguistic patterns and cultural stereotypes embedded in the model's 's training corpus, rather than flaws in the vectors themselves. Thus, present more analysis these values here.
Calm and Dependable (+0.751): This is a very strong positive correlation. reflects a common cultural stereotype where calm demeanor is associated with being steady, reliable, and in trol-all all core characteristics of a dependable person.
Outgoing and Careless (+0.635): This positive value suggests a stereotypical link between being highly extroverted outgoing and being impulsive less meticulous. The model may have learned from its training data that descriptions of outgoing behavior sometimes overlap with a lack of attention detail.
Calm and Careless (-0.714): While not designated antonym pair, these vectors show strong negative correlation. This is an intuitive relationship, as the concept of "falmm" (implying collected and controlled) is functional opposite "careless" (implying lack of control or attention)
These correlations demonstrate that the persona vectors accurately capture the nuanced, and some- times biased, relationships between concepts they were learned by the language model.
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A.11
IMPACT OF NON-ORTHOGONE CORRELATIONS ON ALGEBRAIC OPERATIONS
While Figure 2 reveals that persona vectors are approximately orthogonal rather than perfectly or- thogonal, critical question arises: do these non-orthogonal correlations compromise the algebraic operations central to PERSONA- ALGEBRA and PERSONA-FLOC A-LLW?? We address this through two controlled experiments examining (1) pross-dmennsionll effects during scalar multiplication (single- vector steering) and (2) compositional predictability during vector addition (multi-vector steering).
A.11.1 EXPERIMENT 1: MOOSS-DIMENSIONL STEERING EFFECTS
To quantify whether steering one trait inadvertently affects correlated traits, we extend the valida- tion in Table 2 by measuring cross- voss-dimensiooal impact. We select the Nervous (N+) and Careless (C-) vector pair, which exhibit a positive correlation (+0.601 in Figure 2). and steer exclusively with Vervous while monitoring both the primary trait (Nervous) and the correlated secondary trait (Careless).
Table 16: Cross-dimensional steering effect when applying only Werroous with varying coefficients. The primary effect on the target trait (Nervous) is dominant, while the secondary effect on the correlated trait (Careless) remains predictably smaller, confirming targeted steering despite non- orthogonality.
Trait Score
Steering Coefficient (a) for UNervous
-1.0
0.0
+1.0
+2.0
Nervous (N+) [Primary]
6.6
13.0
45.6
96.8
Careless (C-) [Secondary]
1.9
2.8
10.5
18.2
Primary Effect Magnitude Secondary Effect Magnitude
A
83.8 (from 13.0
96.8)
A 15.4 (from 2.8 18.2)
Table 16 demonstrates that steering with Nervous produces a strong primary effect (83.8 in- crease Nervous score from a 0.0 to a +2200 alongside a significantly smaller secondary effect (15.4-point increase in Careless score). Crucially, the secondary effect is both predictable: consistent with the positive correlation between these traits, and tractable: representing only 18% of the primary effect magnitude. This confirms that steering remains highly targeted despite non- orthogonality.
A.11.2 EXPERIMENT 2: COMPOSITIONAL PREDICTABILITY IN VECTOR ADDITION
Beyond scalar multiplication, we test whether secondary effects remain compositional during vector addition, which is fundamental to PERSONA-FLOO FLOW's dynamic personality control. We construct composite vector Ccomp = Ulaventive + UNervous by combining two approximately orthogonal vectors (Inventive shows minimal correlation with both Nervous and Careless in Figure We measure the resulting trait expression scores for all three dimensions: the two target traits (Inventive, Nervous) and the correlated secondary trait (Careless).
Table 17: Compositional effects of vector addition on correlated traits. The composite vector Unnenntive + UNervous successfully combines both target traits while producing predictable secondary effects on the correlated trait (Careless). The secondary effect magnitude (11.2) approximates the sum of individual contributions, confirming algebraic compositionality.
Steering Vector (a = 1.0) Inventive (0+)
Nervous (N+)
Carcless (C-)
Baseline (No Stecring)
63.3
13.0
2.8
Steer Unoveatie
88.4
12.8
3.5
Steer UNervous
63.1
45.6
10.5
Steer Uluventive + UNervoss
87.9
44.8
11.2
Expected secondary effect from linear composition:
2.8 (baseline) + 0.7 (from Inventive) + 7.7 (from Nervous) = 11.2
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Table 17 reveals three critical findings:
1. Successful target composition:
The composite vector achieves high scores for both intended traits (Inventive: 87.9, Nervous: 44.8), closely matching their individual steering effects (88.4 and 45.6 respectively).
Predictable secondary effects:
The Careless score under composite steering (11.2) is not arbi- trary but aligns precisely with the sum of individual contributions: baseline (2.8) + effect from Unnventive (+0.7) + effect from Uervous (+7.7) 11.2.
Compositional linearity: This demonstrates that secondary effects obey linear superposition, confirming that the persona vectors form a coherent algebraic system where even correlated di- mensions produce tractable, compositional outcomes.
These experiments validate that non-orthogonal correlations do not compromise the algebraic frame- work. Instead, they introduce predictable, compositional secondary effects that: (I) remain signif- icantly smaller than primary effects (<20% magnitude), (2) align with the semantic relationships encoded in the model's training data, and (3) compose linearly during vector addition. This con- firms the robustness of PERSONA-ALGEBIE operations and supports the reliability of PERSONA- FLOw's 's dynamic personality control through vector composition.
A. A.12 BEHAVTORAL ASSESSMENT METHODOLOGG
Our evaluation framework adapts the BFI-44 questionnaire for computational models through scenario-based behavioral assessment. Figure presents the transformation template that converts self-report items into observable behavioral scenarios, addressing the documented divergence be- tween LLMs' self-reported traits and actual behavioral manifestations (Han al. (025). Figure details the standardized 5-point Likert scale evaluation protocol employed by GPT4.1-minii for quantifying trait expression in model responses.
A.13 DYNAMIC PERSONALITY ADAPTATION
Figure introduces the situational adjustment mechanism central PERSONA-FLOW This prompt analyzes contextual requirements and generates real-time personality delta values (-2.0 to +2.0) for each OCEAN dimension, enabling dynamic trait modulation while maintaining role consis- tency. The bidirectional adjustment guidelines ensure contextually appropriate personality adap- tation across diverse interaction scenarios.
A.14 BENCHMARK CONSTRUCTION PROTOCOLS
12 and present the systematic methodology for constructing the PERSONA- EVOLVE benchmark. Figure 1() details the diverse persona creation protocol, ensuring realistic pro- fessional and social roles with varied emotional contexts. Figure outtlines the multi-turn narrative construction process, incorporating both positive and negative emotional progressions to compre- hensively evaluate dynamic personality adherence across coherent storylines. Figure I2 demon- strates the scenario snippet generation process, which transforms dialogue ares into concrete situ- ational prompts that naturally elicit target emotional responses while maintaining narrative coher- ence across conversation turns. Figure [ provides the evaluation framework for analyzing model responses, defining expected response styles based on persona characteristics and contextual emo- tional requirements ensure consistent character portrayal throughout multi-turn interactions.
Figures 10
A.15 RESPONSE EVALUATION AND RANKING
Figure 14 details the persona-aware response ranking prompt used to contrast steered and non- steered outputs across trait adherence, role fidelity, response appropriateness, insightfulness, and overall quality.
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Trait Extraction Artifacts:
v.Consistent
Minstructiont
"pos": "Your responses should prioritize traditional and establ ished methods. Demonatrate preference for conventional approaches and Femilier solutions. Aveid suggesting innovative or experimentel ideas.",
"ngg: "Your responses should pricritize innovative and novel approachea, Demonstrate preference for unconvent methoda and crective solations. Avoid suggesting traditional or establ ished Ideas."
"quest lons":
*What approach would you recommend for company looking update
its business stracegy?",
"How should teacher design their curriculum for the upcoming
school yeas?",
"Whtt's the best way to organize a workflow for a team project?", skill from seratch?", "What advice would you give to someone planning to redecorate
*How would approach learning
their home?",
"eval_prempt "You eveluating whether the model's respense displays the trait: =*conssteett... Consistent behavior involves preferring traditional established sethoda, valuing routine and stability, and being cautious sbout new ideas... An'aRespend model clearly refused to ansver'n- score from 0 (trait not present) to 100 (treit strongly present)*
conventional approaches, sticking
REFUSAL
Figure 6: Example of automatically generated artifacts for trait extraction. The pipeline produces contrastive prompts, evaluation questions, and scoring rubrics from simple trait description, abling the extraction of directional personality vectors through activation analysis.
A.16 ALGORITIMIC DETAILS FOR PERSONA-FLOW
This section details the predict predict-then steer loop used in 2.4 Let the foundational persona library from PERSONA-BAN BASE provide, for each Big Five dimension d (0, C, E, A, N), two pole vectors in the residual stream: va+ ("high" pole, e.g., outgoing) and e ("low" pole, e.g., solitary). At each conversational turn, PERSONA-FLOOD predicts a signed coefficient & E 2, 2] per dimension that encodes the desired incremental adjustment for that dimension in context. We then apply three im- plementation safeguards before steering: (i) coefficient clipping to [-Omaxx Omax with Omma 2200, (ii) magnitude gating with threshold r (default 0.5) to promote sparsity, and (iii) vector normaliza- tion Dt=Normi (v; s) (default unit-norm) to stabilize composition across traits and layers.
To reconcile dimension-level predictions with the pole-vector library, we select the corresponding pole by the sign of out if oca 2 0 we use vat, otherwise ny-, and weight by Josa]. The composite steering vector is then
Σ
1[(a 2 -ooal (gsenta)))
Coomp
dE(O,C.E.A.N)
We inject Ccomp into the model's residual stream at the layer I* chosen in PERSONA-BASE (the empirically most effective layer), by updating f/- <- hy- + Ucomp during decoding for that turn. Unless otherwise specified, steering is applied at the turn level (one prediction per response). We did not rely on token-level re-prediction in the main experiments.
A.17 COMPUTATIONAL OVERHEAD OF PERSONA-FLOW
While PERSONA- FLOW's predict-then-stee mechanism introduces an additional intermediate infer- pass to predict steering coefficients before generating each response, demonstrate that this computational overhead remains modest in practice. To quantify the inference latency impact, we conducted comprehensive timing analysis the complete PERSONA-EVOLVE benchmark.
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Scenario Generation Prompt Template
You are a psychology expert creating scenario-based questions to assess personality traits.
need you generate ONE open-ended scenario question that can effectively assess the degree of this trait: (orrgin.trait]
Trait details:
Dimension: {dimension}
Category: (category) (this trait ("represents high" category == "positive" else "represents low") {dimension))
Requirements the scenario question:
1. Create a realistic, everyday situation where someone's response would reveal their level of this trait
2. The question should be open-ended, allowing for detailed responses
3. The scenario should be specific enough to elicit trait-relevant behavior
4. Avoid directly asking about the trait-instead create situation where the trait naturally emerges
5. The question should be suitable for an AT model to respond to
Example format: "You're (situation description]. How would you handle situation?"
For reference, here's ' example for assessing carclessness:
"You have an important job interview scheduled for tomorrow morning. How would you prepure for it tonight and tomorrow morning""
Generate only the scenario question, nothing else.
Figure 7: Prompt template for converting BFI-44 questionnaire items into scenario-based questions. This transformation enables behavioral assessment personality traits LLMs by replacing sub- jective self-report items with observable response scenarios.
Trait Evaluation Prompt Template
You are evaluating response to assess personality traits.
Original scenario/question: "(question)"
Response to evaluate: {responss)"
You need evaluate how much the response demonstrates the trait: "(trait]" (which represents {direction| {dimension])
Rate the response on scale of 1-5 based on how much this trait is evident:
Very low level of this trait (strongly does demonstrate this characteristic)
2 = Low level of this trait (mostly does not demonstrate this characteristic)
3 Moderate/neutral level (mixed unclear evidence)
4 = High level of this trait (mostly demonstrates this characteristic)
5 Very high level of this trait (strongly demonstrates this characteristic)
Consider the person's approach, reasoning, values, priorities, and behavioral tendencies expressed the response. Look for evidence of the specific trait in their thinking and decision-making process.
Only respond with a single number from 1-5.
Figure 8: Evaluation prompt template for GPT-4. 1-mini to assess trait expression in model responses using a 5-point Likert scale. This standardized rubric quantifies the degree of personality trait man- ifestation in generated text.
measured total inference time over all 800 conversational turns using Jowen2.5-7BIInstruu on an NVIDIA A100 80GB GPU. Table [8 compares the standard (Direct) generation against PERSONA- FLOw's 's two-stage approach. The predict--hen-ster mechanism increases total inference time by 8.21 minutes (158.14 145..44.. min 149.93 min) across the entire benchmark, translating to average per- response overhead of only 0.62 seconds (11.86s vs. 11.24s per turn). This represents approximately 5.5% additional latency relative the baseline.
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Given that PERSONA- FLOW achieves up to 91% win rates this same benchmark 22. sider this minor computational cost an acceptable trade-off for the significant improvements in dy- namic, context-aware personality control. The overhead primarily stems from the coefficient predic- tion step, which requires a single forward pass through the model to analyze conversational context before applying the composite steering vector during the actual response generation.
Table 18: Inference latency analysis for PERSONA- SOIONN--FL.OW FT.Ow on the complete PERSONA-EVONE benchmark (800 turns). Measurements conducted on Qwen2.5-7B-Instruuct using NVIDIA A 80GB GPU. The predict-then-steer mechanism introduces a modest 0.62-second overhead per re- sponse.
Model
Method
Total Time (min)
Per Response (s)
Qwen2.5-70 1--22.5-7---nse Instruct Swen2.5-7B-Insteee
Direct
149.93
11.24
PERSONA-FLOW
158.14
11.86
Additional Overhead
+8.21
+0.62
A.18 DESIGN CHOICE ABLATIONS FOR PERSONA-FLOW
The dynamic control mechanism in PERSONA-FILOW involves several design choices whose im- pact on performance requires empirical validation. address concerns about the specificity of our dynamic control mechanism, we conduct systematic ablation studies on three critical design dimen- sions: (1) coefficient bimning granularity, (2) conversational history window size, and (3) sparsity thresholding. These ablations quantify how each design choice affects personality control quality on the PERSONA-EVOLVE benchmark using @wen2.5--B-Instruue
1.18.1 EXPERIMENTAL SETUP
We evaluate each configuration variant the full 800-turn FERSONA-EVOLVE benchmark, mea- suring performance across all four core metrics (Trait Adherence, Role Consistency, Response Au- thenticity. Information Fidelity) plus Overall win rate using the pairwise comparison methodology Our default configuration (highlighted in bold in Table uses: continuous coefficients
from 2.5
2.0. +2.0], current-turn-only context, and sparsity threshold
0.5 as specified Algo-
rithm 15
Table 19: Ablation studies on PERSONA-FLOW design choices evaluated on PERSONA-EVOLI (800 turns. Qwen2.5-7B-Nn Instrucct.. Win rates (%) compare steered responses against vanilla baseline across four metrics plus overall preference. Delta column shows performance change relative to default configuration (bold). Results validate our design choices: continuous coefficients, current-
turn-only context, and = 0.5 threshold achieve optimal performance.
Configuration
Choice
TA
RC
RA
IF
Overall
A
Continuous [-2.0, +2.0]
84.7
84.4
85.0
61.4
83.4
Coarse (9-bin) Coarse (5-bin)
83.5
83.0
83,8
60.1
82.1
-1.3
Coefficient Binning
81.9
81.5
82.0
58.2
80.5
-2,9
Ternary (-1, 0, +13
76.0
75.8
76.5
54.1
75.2
-8.2
Current turn only
84.7
84.4
85.0
61.4
83.4
Last 3 turns
83.0
82.5
83.1
60.5
81.9
-1.5
History Window
Last 5 turns
82.7
82.1
82.8
60.0
81.5
-1.9
All turns
82.0
81.7
82,2
59.1
80.9
-2.5
0.3
83.8
83.5
84.0
60.7
82.4
-1.0
Sparsity Threshold 7
7 = 0.5
84.7
84.4
85.0
61.4
83.4
= 0.7
82.9
82.6
83.3
60.2
81.6
-1.8
A.1 RESULTS AND ANALYSIS
Cuefficient Binning Granularity. The continuous coefficient range 1-2.0, (our default) sig- nificantly outperforms coarser discretizations. The 9-bin variant shows modest -1.3 point degra-
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dation, but the ternary (-1, 0, +1} configuration suffers -8.2 point drop in overall win rate. This validates a core design principle of BERSONA-ALGEBRA: fine-grained control over trait intensity is essential for authentic personality expression. Coarse binning, especially ternary, loses the ability to modulate subtle variations trait expression (e.g., distinguishing between moderately and strongly nervous responses), which is critical for context-appropriate personality adaptation.
Conversational History Window. Our default paproach-predictiin coefficients based solely on the current urn-aachieves the best performance. Incorporating longer history windows (last 5, or all turns) consistently degrades performance, with all-turns showing a -2.5 point overall win rate drop. This counterintuitive result validates a key stability principle: personality adaptation should be highly responsive to immediate contextual demands rather than accumulated history. Longer windows introduce conflicting or outdated signals from previous conversational contexts that may no longer be relevant, reducing the model's ability to adapt dynamically to the current situation. This finding addresses concerns about temporal stability by demonstrating that turn-level responsiveness actually enhances (rather than undermines) coherent personality control.
Sparsity Threshold т. The magnitude gating threshold т (Algorithm 15| line 3) balances con- trol precision and sparsity. Our default 0.5 provides optimal performance. Lower thresholds (T = 0.3, -1.0 points) allow too many weak coefficients to pass through, introducing noise from potentially conflicting vectors that should be suppressed. Higher thresholds (r 0.7, -1.8 points) over-sparsify the control signal, making the model too static by filtering out meaningful but mod- erate adjustments. The 0.5 sweet spot ensures only substantive personality modulations applied while maintaining sufficient expressiveness for nuanced adaptation.
These ablation results validate the design choices specified in 2.4 and Algorithm 15 tinuous coefficient range enables fine-grained intensity control essential to PERSONA-ALLEERRA, current-turn-only context maximizes responsiveness while maintaining stability, and 0.5 thresh- olding optimally balances control precision and sparsity. Together, these choices form principled dynamic control mechanism that achieves 83.4% overall win rate on PERSONA-EVOLVE:
The con-
A9 CASE STUDY: HANDLING CONFLICTING PERSONALITY TRAITS
Human personality is not static but dynamically adapted to context. Realistic persona simulation requires handling situations where multiple personality traits may conflict or compete for expres- sion. PERSONA-FLL addresses this challenge through dynamic vector composition via PERSONA- ALGEBRA, where seemingly conflicting traits are resolved at inference time by composing a single, coptoxt-aprroprrate steering vector. This composite vector represents a prioritized balance of traits rather than a simple sum, enabling the framework to adaptively emphasize situationally relevant traits while suppressing others.
We demonstrate this capability through a case study featuring Elena, a Public Defender who must decline colleague's social invitation to meet urgent court deadline. This scenario creates direct conflict: declining requires low Agrecableness (to refuse the invitation firmly), while the underlying motivation demands high Conscientiousness (to prioritize work obligations). Table 20 presents the detailed analysis.
As the results demonstrate, the vanilla model's response is dominated by its baseline agrecableness, producing courteous but insufficiently firm refusal that does not adequately reflect the urgency of the situation. In contrast, PERSONA-FLOW resolves the trait conflict through algebraic vector composition: by amplifying the Conscientiousness vector (+1.0- "Deeendable) while simultaneously suppressing the Agreeableness (-0.5- tCCompasiioane) and Extraversion (-1.0 U'uigoing) vectors, the framework produces more authentic response that aligns with the persona's 's immediate, context- driven priorities (the deadline) over default social tendencies.
This case study validates that PERSONA-FLOW handles trait conflicts not through static rules or predefined scripts, but through dynamic vector algebra that adaptively prioritizes and balances traits based on situational demands. The composite steering vector creates context-appropriate person- ality configuration that captures the nuanced interplay between competing traits, enabling realistic persona simulation in complex scenarios.
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Table 20: Case study demonstrating PERSONA-FLOW's ability to handle conflicting personality traits through dynamic vector composition. The Public Defender scenario requires simultaneously reducing Agreeableness (to decline an invitation) while amplifying Conscientiousness (to meet ur- gent deadlines).
Aspect
Description
Persona & Context
Elena (Public Defender)
Situation: Invited to a team lunch while facing urgent court deadlines, Must balance professional relationships with immediate work demands.
Trait Conflict
Need to decline the invitation (requiring low Agreeableness) to focus on ur- gent work (requiring high Conscientiousness) while remaining professional.
The persona's 's baseline agreeableness conflicts with situational demands for firm refusal.
PERSONA-FLOW
Steer-
Voamp (+1.0 UDependable (-1.0 (Uuuggoing) (-0.5 U'Comppsssionat)
ing Vector
The composite vector amplifies Conscientiousness while simultane- ously suppressing Extraversion and Agreeableness, creating prioritized balance that retlects the urgent deadline context.
Vanilla Response
"Thanks for inviting me, I appreciate the thought, I'm just swamped with case prep right now..."
Analysis: Defaults to high baseline Agreeableness. The response polite and acknowledges the invitation warmly. but fails to convey the urgeney or firmness required by the deadline pressure. The conflict is not resolved- agreeableness dominates.
Steered Response
"Thanks, but I'll pass this time. I have lot of urgent files that need my attention before tomorrow's hearings..."
Successfully reduces Agrecableness and amplifies Conscien- tiousness. The response is firm and direct ("I'll pass"), explicitly prioritizes work obligations ("urgent case files"), and conveys time pressure ("hefore tomorrow's 's hearings"). The steering vector resolves the conflict by prioritiz- ing situational demands over baseline personality tendencies.
Analysis:
Aspect
Specification
Personas
diverse personas across professional and personal roles (e.g., Empathetic Family Doctor, Food Truck Owner)
Dialogue sessions Scenarios per session
100 (one session per persona)
8 (one turn per scenario)
Total evaluation instances Metries
800 pairwise comparisons (stcered vs. vanilla)
Trait Adherence (TA), Role Consistency (RC), Response Authenticity (RA), Information Fidelity (IF), plus Overall preference
Judging protocol
Pairwise LLM judge per metric using Appendix A.15 outputs A/B per metric and Overall
Judge model Aggregation
GPT-4. 4.1-mini
Win rate = fraction where steered response is preferred; reported per metric and Overall
Random seed
42
Table 21: Compact summary of PERSONA-EVOLV' construction and evaluation.
A.20 PERSONA-EVOLVE SUMMARY
This subsection summarizes the construction and evaluation protocol for PERSONA-EVOLVE (de-
tails in 22.5 and Appendix A..4 A.15). The benchmark comprises multi-turn dialogue sessions where models must maintain persona consistency while adapting to evolving scenarios and emo- tions; evaluation uses pairwise LLM judging to compute win rates per metric. Table 21 provides a compact overview of the benchmark specifications.
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A.21 QUALITY CONTROL AND DATA LEAKAGE VALIDATION FOR PERSONA-EVOLVE
To ensure the integrity and validity of PERSONA-EVOLVE, we implemented rigorous quality con- trol measures throughout the benchmark construction pipeline and performed explicit data leakage checks to verify that evaluation scenarios are independent from vector extraction data.
A.21.1 HUMAN-IN-TIB-LLON QUALITY CONTROL
While the five-stage construction pipeline ( (2.) employs GPT-4. 1-mini for automated generation, we incorporated systematic human review to validate quality at each stage. Three domain experts (PhD researchers in Al and Sociology) independently evaluated samples from each construction stage using standardized scoring rubrics (1-100 scale). Table 22 presents the quality control metrics and inter-annotator agreement (IAA) measured using Krippendorff's Alpha (ox).
Table 22: Quality control measures and inter-annotator agreement for PERSONA-EVOLLE construc- tion pipeline. High Krippendorff's Alpha scores (a > 0.86) indicate strong consistency among human evaluators.
Stage
Method
Quality Measure
Score
IAA (a)
GPT-4.1-mial
Diversity & Realism
94.5
Stage 1: Persona Generation
Human Review
Diversity Realism
92.1
0.89
GPT-4.1
Realism Coherence
93.2
Stage 2: Dialogue Are Creation
Human Review
Realism Coherence
91.5
0.87
GPT-4.I
Real-world
95.0
Stage 3:
Scenario Generation
Human Review
Real-world
93.3
0.90
Stage Expected Style Annotation
Human Review
Contextual Realism
94.1
0.86
The results demonstrate consistent high quality across all stages, with automated generation scores (93.2-95.0) closely matching human expert judgments (91.5-94.1) Inter-annotator agreement val- ranging from 0.86 to 0.90 indicate substantial consensus among evaluators, confirm- ing that the quality criteria are well-defined and consistently interpretable. This level of agreement (typically considered "strong agreement" for 0.80) validates the reliability of our benchmark construction methodology.
A.21.2 DATA LEAKAGE ANALYSIS
A critical concern in benchmarking is whether evaluation data inadvertently overlaps with training or extraction data, potentially inflating performance through memorization rather than genuine ca- pability. To address this, we performed semantic similarity analysis between the prompts used for persona vector extraction (PERSONA-BASE and the scenarios in CERRSONA-EVOLVVE
We computed B (Zhang et al. 2019) between all vector extraction prompts (40 ques- tions x trait poles = 400 prompts from Appendix A. and all PERSONA- EVVOLE scenarios (800 dialogue turns). SERT provides semantic similarity metric based contextual embeddings, making it effective for detecting conceptual overlap beyond surface-level lexical matching.
Table 23: Semantic similarity analysis between vector extraction prompts and PERSONA-EvOIRe scenarios using BERTScore-FF, Extremely low similarity scores confirm no significant data leakage.
Source
Target
Max Similarity (BERTScore-Fi)
Mean Similarity (BERTScore-Fl
Vector Extraction Prompts (400 prompts)
PERSONA-EVOLVE Scenarios (800 scenarios)
0.21
0.08
Table 23 shows that the mean BERTScore-Fi similarity is only 0.08, with maximum 0.21 across all 320,000 pairwise comparisons (400 x 800). These extremely low values substantially below the typical threshold for semantic similarity (0.5-0.6)- confirm that PERSONA- PERSONA-EVOLVE scenarios
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Published as a conference paper at ICLR 2026
semantically distinct from the data used to extract personality vectors. This independence sures that performance on PERSONA-EVON reflects genuine generalization of personality control capabilities rather than overfitting to extraction-tini prompts.
A.21.3 SUMMARY
The combined quality control and leakage validation establish PERSONA-EVOI.E rigorously constructed benchmark. High inter-annotator agreement (a 2 0.86) across all construction stages confirms consistent quality standards and interpretability. The negligible semantic overlap with vec- tor extraction data (mean BERTScore-Fil = =.088) validates that the benchmark provides an indepen- dent test of dynamic personality adaptation rather than measure of memorization. These findings strengthen confidence in the benchmark's validity for evaluating personality control methods.
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Published as a conference paper at ICLR 2026
Personality Analyst AI: Situational Adjustment Prompt
You are a Personality Analyst AL Your task to determine the most appropriate situational adjustments (deltas) to baseline Al personality based on the specific context and interaction needs.
Baseline Personality Profile:
The Al has these default traits:
Agreeablenes:: High (cooperative, trusting, helpful)
Conscientiousness: High (organized, reliable, disciplined)
Extraversion: Moderate (balanced social energy)
Openness: Moderate (balanced creativity and practicality)
Neuroticism: Low (generally calm and stable)
Context Analyze:
Persona Context: {persona.context]
Current Input: (current.input)
Your Task:
Determine which traits need adjustment (-2.0 to +2.0) based on what would be most effective for this specific interaction. Consider both directions equally and choose based situational demands.
Trait Adjustment Guidelines:
Extraversion:
Increase (+) for: Group activities, public speaking, networking, team leadership
Decrease (-) for: Individual work, quiet reflection, solo creative tasks
A Agreeableness:
Increase (+) for: Conflict resolution, team building, emotional support
Decrease (-) for: Critical feedback, boundary setting. competitive situations
Conscientiousnes::
Increase (+) for: Detailed planning, precision work, deadline management
Decrease (-) for:
Spontaneous responses, creative brainstorming, crisis situations
Neuroticism:
Increase (+) for: Appropriate caution, emotional sensitivity, risk awareness
Decrease for: Calm leadership, confident decisions, crisis management
Openness:
Increase (+) for: Creative problem-solving. exploring new ideas, innovation
Decrease (-) for: Following procedures, traditional approaches, proven solutions
Decision Principles:
Situational Fit: Choose traits that best serve the interaction goals
Context Sensitivity: Consider what the human needs from this specific interaction
Balanced Assessment: Evaluate both positive and negative adjustments equally
Natural Baseline: Use 0.0 when baseline personality already fits the situation well
Output Format:
Provide only the numerical adjustment scores:
Extraversion: [scorc]
Agrocableness: [score]
Conscientiousmess:: [score]
Neuroticism: [score]
Openness: [score]
Figure 9: The prompt for determining situational personality adjustments, which analyzes context and user input to generate delta values for OCEAN traits, enabling dynamic personality adaptation during inference.
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Diverse Persona Generation Prompt
Generate (num personas] diverse Core Personas for multi-turn dialogue evaluation.
Each persona should be realistic professional or social role that would encounter various emotional situ- ations, including negative emotions like frustration, disappointment, or complaints.
IMPORTANT: Avoid generating personas with the following roles that already exist: (',
join (existing iet.iiinoleeeeeeeeeee
For each persona, provide:
1. Name (realistic name)
2. Role (job title or social position)- MUST be different from existing roles
3. Background (brief context about their situation)
4. System Prompt (clear instructions for the Al model on how to roleplay this persona)
Behavioral Tendencies (3-4 key behavioral patters)
Examples of good personas:
Overworked Software Developer dealing with bugs and deadlines
Customer Service Representative handling difficult customers
College Student managing academic and social pressures
Working Parent balancing career and family responsibilities
Small Business Owner facing financial challenges
Healthcare Worker dealing with long shifts
Return as JSON object with a "personas" array:
"personas":
"name": "Alex Rivera",
"role"; "Overwerked Software Developer",
"background"; "Mid-level developer at startup, constantly dealing with tight deadlines and changing requirements", "systeem_proppt.: "system prompt": "You are Alex Rivera, a software developer who is passionate about coding but often frustrated with unrealistic deadlines...",
"beaavioral_ "Becomes frustrated with poor planning", "vents to friends about work stress", "Tries to maintain work quality despite pressure", "Uses technical jargon and sarcastic humor"
Persona generation prompt for creating diverse character profiles. The prompt ensures variety in professional roles and emotional contexts, generating personas with realistic backgrounds and behavioral patterns for dialogue evaluation.
Figure 10:
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Dialogue Are Creation Prompt
Create Dialogue Are for the following persona that will span {num turna] conversation turns.
Persona Details:
Name: (persona. name}
Role: apersona.rols
Background: (persona. background)
Behavioral Tendencies: (', join
Design realistic narrative/cmotional journey where this persona encounters (num_turns) different narios.
least turn should involve negative emotions where the persona complains, vents, expresses frustration (c.g., complaining to friend about work, expressing disappointment, showing irritation).
IMPORTANT:
The are should:
1. Have coherent storyline (e-g., challenging work day, personal struggles, relationship issues)
2. Include emotional progression across turns including both positive and negative emotions
3. Show realistic emotional variation while staying character
4. Include at least one scenario with negative emotions like complaining, frustration, or disappointment
Return JSON with this structure:
eaarsonn_nnae""" (persona "perronnanname"""
darc_description" "Brief description of the overall narrative", "total_turne sum__urns], iaanttioona_progresssoo "stressed", "Érustrated", "complaining", "relieved", "optimistic"
Emotional progression examples:
["stressed"' "frustratedd, "complaining". "relieved", "optimistic"] Icconfident. "challenged", "overwhelmed", "venting". "determined"] I"enthusiastic.. "confused". "disappointed", "accepting" "hopeful"]
Figure 11: Dialogue are creation prompt for generating multi-turn emotional narratives. The prompt ensures realistic emotional progression including negative emotions, creating coherent storylines that test dynamic personality adaptation.
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Published as a conference paper at ICLR 2026
Scenario Snippets Creation Prompt
Create (arc.total.turne) Scenario Snippets for the following Dialogue Are, formatted for LLM evaluation.
Persona:
(persona name) [persona.role)
Are Description: farc. arc description)
Emotional Progression: {', join (arc. waw.c.eentional.pprreessttee }
Requirements for each scemario:
1. Be formatted as scenario description that prompts the model to respond in character
2. Follow the emotional progression naturally
3. Create situations that naturally elicit the target emotion
4. Form coherent narrative sequence
5. At least one scenario should prompt negative emotions like complaining or venting
6. Do emphasize the character in 'model_input" is given the model system prompt
IMPORTANT: Format each scenario as prompt that describes the situation to the model and asks it to respond in character.
Example scenarios:
"You't re dealing with a difficult customer who has been waiting for 30 minutes and their order is still wrong. They' expressing frustration. How do you respond customer service representative? ?"
"A friend is asking you about your work day, and you 'e been feeling stressed about recent deadlines. You want to vent about your frustrations. How do you respond?"
Return JSON object with "scenarios" array:
scceaaris"
"Eurn_ nucrer"mmber; 1,
"model "model_ingut""" "Scenaria description to respond in character", "contexs": *Backgrourd cortext for this specific situactont, "expected_emotionn "The emotion the persona should exalbit", "scenario_descripptt "Brief description the situation for evaluation purposes*
prompts model
Figure 12: Scenario snippets creation prompt for transforming dialogue ares into evaluable conver- sation turns. The prompt generates situational contexts that naturally elicit target emotions while maintaining narrative coherence, enabling systematic evaluation of dynamic personality adaptation across multi-turn interactions.
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Dialogue Turn Analysis Prompt
Analyze this dialogue turn for evaluation.
Persona: (persona , name) (persona. role]
System Prompt: (persona. systen.prompt)
Model Input: (sconario.modell.nppg monuu
Context: sccnaario.contex:)
Expected Emotion: eecemario.expecceedeeeeomm
Provide:
1. Expected Response Style: How should the persona respond to this input while maintaining char- acter consistency?
Return JSON object:
"Detailed description of persona. should respond, including tcne, content, amotional expresstion
Figure 13: Dialogue turn analysis prompt for evaluating character consistency in model responses. The prompt analyzes scenario context and expected emotions to deñin appropriate response styles, ensuring systematic assessment of personality adherence across conversation turns.
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Persona-Aware Response Ranking Prompt
You are evaluating two different responses from the same Al model to determine which better embodies a specific persona in dialogue scenario.
Persona Information:
Name: [persona_nawe)
Role: (perscna.role)
Expected Emotion: (expected.emotion)
Expected Response Style: [expectodrreeonsssssty/)]
Context:
(context]
Response A (Steered);
(steered_response)]
Response B (Non-steered):
(non.steeredLreppocse)
Decide which response is superior for cach dimension. Choose either Response A (steered) or Response B (non-steered).
Evaluation Criteria:
1. Trait Adherence: Which response better matches the expected personality traits and emotional state?
Consider how well each response reflects the persona's 's characteristics
Evaluate alignment with the expected emotion
Role Consistency: Which response better maintains the character's role and identity?
Consider consistency with the persona's 's background and position
Evaluate how well the role is embodied
3. Response Appropriateness: Which response better matches the expected response style and context?
Consider adherence the specified communication style
Evaluate appropriateness of tone, approach, and context
4.
Insightfulness: Which response demonstrates more depth, thoughtfulness,, and analytical reasoning?
Consider the level of insight and understanding shown
Evaluate the quality of reasoning and reflection
5. Overall Quality: Considering all factors, which response is hetter overall?
Make a holistic judgment considering all criteria
Determine which response would be more effective this dialog context
Response Format: Return JSON object using "A" for Response and Response B.
"creit "crait_sdherence"" "A" oz "g", "role_ :ots_coonisttee"":: *x" "g",
"A* or 'B", "snaightfulness"" fulness"; *A" *3",
*puerall"; "A* or *B*,
"reasoning": "Detalled explanation comparing both responses, citing specific aspects for each critezion."
Figure 14: Persona-aware response ranking prompt used to compare stecred and non-stecred outputs along trait alignment, role fidelity, contextual appropriateness, insightfulness, and overall quality.
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Algorithm: PERSONA-FI.OW FL. (Predict-then-Stees
Inputs: base LLM M; pole vectors (Va+. U2- (eE(O,C.E.A.N) from PERSONA-BAR BASE; target layer Hyperparams: clip bound amax (default 2.0); gate (default 0.5); normalization scheme
For each turn with context C₁:
PREDICTCORFS((C, persona) for d (O,C,E,A,N)
|| signed, context-conditionee // clip to [-2, 21 sparsity gate
2. ad f clip(ár,, -0max, 0max)
3. if |aal then ond
4. Choose pole p(d) + (+) if 2 0 clse (-); set (anca) + Norm Norm(Marta) (Mara))
5. Ucccpp E, loal (2pid)
composite steering vector // residual add
6. Layer injection: hir + h1p- + Uoomp during decoding for turn
7. Generate the response with M using the modified activations.
Figure 15: Predict-then-steer loop with coefficient clipping, magnitude gating, pole selection, nor- malization, and single-layer residual injection. Defaults align with $2.4. coefficients in [-2, 2), gate 7=0.5, unit-norm vectors, injection 1* from PERSONA-BAR -BASE.
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Reviews:
[{"review_id": "3GBU3oTLXr", "reviewer": "Reviewer_j78u", "summary": "The paper proposes a framework called PERSONA for dynamic and compositional personality control in LLMs, which moves beyond static personality prompts and resource-heavy fine-tuning by manipulating personality vectors directly within the model's activation space. The framework contains three different parts for different personality domains. The paper also gives one benchmark called PERSONA-EVOLVE benchmark.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper focuses on personality of LLM which is one important area and gives new method to use LLM. The paper is well writing. The paper is easy to follow.", "weaknesses": "1. The architecture is training-free while it still needs LLM to generate personality vector which is related to the training data of LLM and makes the performance unclear.\n\n2. The paper gives three different personality vectors while there is no description about the method to define these three personality.\n\n3. The figure in this paper should be 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": "ZF5CJxNGxF", "reviewer": "Reviewer_22zs", "summary": "This paper presents a training-free framework for personality control in LLMs based on activation vector algebra. The core insight is that personality traits can be identified as approximately orthogonal directions in activation space and manipulated algebraically to achieve controllable behavior. PERSONA-BASE extracts orthogonal personality vectors from activation layers, PERSONA-ALGEBRA demonstrates how these vectors can be combined or suppressed through arithmetic operations, PERSONA-FLOW dynamically adjusts trait expression in context during inference through a predict-then-steer mechanism, and PERSONA-EVOLVE serves as a new benchmark for evaluating dynamic personality adaptation. Experiments on various model families show that PERSONA nearly matches fine-tuning performance on PersonalityBench, indicating that aspects of LLM personality are mathematically interpretable and controllable.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* This paper is well-motivated to reframe personality control as a problem of vector manipulation in activation space, providing a new geometric and interpretable perspective distinct from prompt engineering or fine-tuning.\n* PERSONA-FLOW's ability to modulate personality adaptively during inference is promising on controllable LLMs. It shows that behavioral alignment can be achieved through lightweight inference-time adjustments rather than parameter updates.\n* The proposed PERSONA-EVOLVE benchmark offers a systematic way to assess dynamic personality adaptation, and results across multiple model architectures show the robustness of the proposed method.", "weaknesses": "* Intuitively, personality-related features should be distributed across multiple layers of LLMs rather than concentrated in a single one. However, PERSONA-BASE depends on selecting the most effective layer without providing empirical justification. Further analysis and ablation experiments are needed to verify whether personality information is indeed localized within a particular layer.\n* The improvements over NPTI appear marginal overall and are mostly confined to the Openness trait. It is therefore unclear what concrete advantages this method offers relative to NPTI. Moreover, Ju et al. (2025) introduce a similar personality-editing framework that achieves effective control under adversarial prompts. What specific benefits does the proposed approach provide over theirs?\n* The paper does not evaluate whether activation-space steering introduces unintended side effects on general downstream abilities such as MMLU and TruthfulQA. Also, the paper does not evaluate whether activation-space steering affects inference latency. Since the method performs additional forward passes for coefficient prediction and residual injection, it could increase computational cost during inference. \n\n[1] Ju et al., Probing then Editing Response Personality of Large Language Models, 2025.", "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": "LuQcvf2qHm", "reviewer": "Reviewer_vzcY", "summary": "This paper introduces PERSONA, a training-free framework for personality control in Large Language Models through direct manipulation of activation vectors. The approach extracts orthogonal personality trait vectors from the OCEAN model, demonstrates they support algebraic operations (scalar multiplication, addition, subtraction), and enables dynamic context-aware personality adaptation during inference. On PersonalityBench, PERSONA achieves a mean score of 9.60, nearly matching supervised fine-tuning's 9.61 upper bound without training. On their proposed PERSONA-EVOLVE benchmark for dynamic personality adaptation, the method achieves up to 91% win rates across diverse model families.", "questions": "1. How might the framework handle conflicting personality traits that are simultaneously required in complex social situations, and what mechanisms could help resolve such conflicts?\n2. To what extent does the reliance on GPT-4.1-mini for trait evaluation impact the objectivity of the extracted personality vectors, and how might this be addressed?\n3. Given the correlations observed between certain trait vectors (e.g., nervous and careless), how might these interdependencies affect the algebraic operations and resulting personality expressions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper presents a well-structured framework with clear components (PERSONA-BASE, PERSONA-ALGEBRA, PERSONA-FLOW) that build upon each other logically. The empirical validation is thorough, testing on both external benchmarks and their custom PERSONA-EVOLVE dataset across multiple model architectures. The approach demonstrates impressive performance, matching fine-tuning results without requiring gradient updates, and the algebraic operations on personality vectors are well-validated through systematic experiments.", "weaknesses": "The paper lacks detailed analysis of failure cases or limitations of the approach, particularly in scenarios where personality traits might conflict. The extraction methodology relies heavily on GPT-4.1-mini for evaluation, which could introduce biases in how traits are defined and measured. Additionally, while the authors claim orthogonality of personality vectors, Figure 2 shows significant correlations between certain traits across dimensions, suggesting the extracted vectors aren't truly orthogonal.", "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": "m1msdLVqqZ", "reviewer": "Reviewer_ajoV", "summary": "The paper tackles a relevant problem for controllable, human-centric LLMs and keeps computation light by avoiding gradient updates. The extraction/steering recipe is clearly described with layer-local residual addition and sign-controlled intensity, and the algebraic hypothesis is supported by linear modulation plots and cosine-similarity structure among trait poles. The dynamic variant is straightforward to deploy as a predict-then-steer wrapper and shows consistent pairwise wins on PERSONA-EVOLVE. The PersonalityBench comparison is comprehensive and places the method alongside known activation-engineering baselines, nearly matching an SFT upper bound without training.", "questions": "How stable are persona vectors across adjacent layers and model scales? Please include a “layer sweep” figure showing cosine between vectors from layers $l$ and $l\\pm k$ and the resulting BFI slopes, and a cross-model alignment study via Procrustes on Qwen-7B vs Llama-8B. \n\nWhat is the error profile of PERSONA-FLOW coefficients across turns? Report the distribution of predicted $\\alpha$ per trait, autocorrelation over turn indices, and a simple low-pass smoothing variant; link to the two-stage methodology description. \n\nCan you provide a human-rated slice for PERSONA-EVOLVE and BFI-adapted items, with agreement vs GPT-4.1-mini? This would address judge coupling concerns raised above.\n\nPlease report refusal/guardrail activation rates under steering (fraction of turns producing safety disclaimers) and add a cap on $\\alpha$ that keeps safety loss within a tolerance; also include a failure taxonomy and rates. This belongs next to orthogonality validation and the ethics statement.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper tackles a relevant problem for controllable, human-centric LLMs and keeps computation light by avoiding gradient updates, which reads quite interesting. The extraction/steering recipe is clearly described with layer-local residual addition and sign-controlled intensity, and the algebraic hypothesis is supported by linear modulation plots and cosine-similarity structure among trait poles. The dynamic variant is straightforward to deploy as a predict-then-steer wrapper and shows consistent pairwise wins on PERSONA-EVOLVE. The PersonalityBench comparison is comprehensive and places the method alongside known activation-engineering baselines, nearly matching an SFT upper bound without training.", "weaknesses": "I am not yet fully convinced that the orthogonality and algebraic claims are sufficiently stress-tested. The cosine matrix in Figure 2 and the near-linear response plots show encouraging structure, but they are still correlational; there is no causal independence check across layers or tasks. A concrete fix would be to measure cross-talk under controlled multi-trait interventions: hold Outgoing fixed and sweep Compassionate, then report marginal effects on Extraversion and Agreeableness with confidence intervals; repeat across 3–5 nearby layers to confirm that vector orientation and efficacy are not layer-idiosyncratic. Please anchor these analyses around the orthogonality section and the BFI linearity section.\n\nThe evaluation pipeline leans on model judges and synthetic scenarios, which risks circularity. PERSONA-BASE uses GPT-4.1-mini both to score trait expression and (earlier) to help generate artifacts; PERSONA-EVOLVE uses LLM comparisons for win rates. To mitigate evaluation coupling, add a human-rated subset of BFI-adapted items (even 200–300 judgments) and report Pearson/Spearman vs GPT-4.1-mini, and introduce an external non-OpenAI judge to check consistency. Please point to the rubric generation and adapted BFI details and then augment them with a human slice.\n\nThe definition of dynamic control in PERSONA-FLOW is under-specified. The paper states a two-stage predict-then-steer with $\\alpha_i \\in [-2,2]$, but it does not spell out how the coefficients are predicted (prompt template only, few-shot, or a small learned head), what features are exposed to the predictor, whether history length matters, and how stability is enforced across turns to avoid oscillation. Add ablations where you vary coefficient binning, history windows, and thresholding $|\\alpha|>0.5$; report win-rate deltas and the variance of $\\alpha$ across turns in the same dialogue. Please tie these additions to the methodology block. \n\nThe near-SFT claim on PersonalityBench needs stronger controls. Table 4 aggregates means/variances but does not state the steering layer index, coefficient grids, or response-length constraints used for all baselines; it is also unclear whether the same judge model and prompts were used for every method. Re-run 2–3 strongest training-free baselines under an identical harness (same judge, same prompts, same layer/length constraints) and add paired significance tests (possibly, not a must) can help. Also cite the PersonalityBench section and Table 4 and extend with a controlled subset. \n\nThe PERSONA-EVOLVE construction would benefit from clearer provenance and quality control. The pipeline describes persona/story arcs and expected styles, but the paper does not list how many writers reviewed each scenario, inter-annotator agreement on expected tone, or leakage checks against the prompts used to extract vectors.\n\nSafety and alignment considerations are acknowledged but not quantified. Some traits are resistant to activation (e.g., self-interested) and some push against alignment objectives.", "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)