Dynamic Personality Adjustment in AI Agents

• Dynamic personality adjustment is the process where AI agents update their trait representations, using frameworks like the Big Five and MBTI to simulate human-like behaviors.
• It employs methods such as incremental updates, reinforcement learning, and activation-level modifications to adapt personality based on real-time stimuli.
• This approach improves realism in gaming, robotics, and conversational systems while addressing challenges like trait interdependency and safety concerns.

Dynamic personality adjustment refers to methods, models, and technical mechanisms by which artificial agents—whether non-player characters (NPCs), conversational agents, robots, or LLMs—can transition or adapt their personalities in response to external stimuli, context, or over time. In computational settings, it typically involves real-time or staged modification of structured personality representations (often parameterized under frameworks such as the Big Five or MBTI), resulting in agents whose behavioral and affective responses evolve alongside their experiences and environment. This topic is central to simulating human-like behaviors, enhancing realism in interactive systems, and supporting adaptive, user-aligned AI.

1. Theoretical Foundations: Models and Representations

Dynamic personality adjustment builds upon established psychological models for human personality, predominantly the Five Factor Model (FFM, or Big Five: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) (Georgeson, 2016, Centellegher et al., 2017, Miyama et al., 2022, Vu et al., 22 Dec 2024, Zhu et al., 21 Aug 2024, Chittem et al., 26 Jun 2025), as well as other frameworks such as MBTI (Cheng et al., 25 Apr 2025) and the 16 Personality Factors (16PF) (Chittem et al., 26 Jun 2025). Each agent or model instance is parameterized with numerical vectors corresponding to trait or facet values.

• In the ExAI engine (Georgeson, 2016), each NPC is represented by 30 FFM facet scores (range 0–100).
• For robots, the Big Five are instantiated as a parameter vector $P = [O, C, E, A, N]$ that governs downstream appraisal and action (Tang et al., 2 Feb 2025).
• The SAC framework (Chittem et al., 26 Jun 2025) extends trait modeling beyond coarse Big Five dimensions to the full 16PF suite, supporting expressive, multi-dimensional control.

Personality representations serve as latent variables, either statically initialized or recalibrated as agents interact with their environments or users.

2. Mechanisms of Dynamic Adjustment

2.1 Incremental and Stimulus-Driven Adjustment in Simulation

Personality adjustment is frequently implemented as a function of interaction history and stimuli:

• In ExAI, facet scores are incrementally updated in response to game events:

$\Delta F_i = k_i \cdot S_i$

where $S_i$ is the stimulus magnitude and $k_i$ is a sensitivity constant (Georgeson, 2016).

• Attitudinal responses (e.g., trust, like) are derived via a scalar product between the current facet vector $\vec{F}$ and response weight vector $\vec{w}$, $R = \vec{F} \cdot \vec{w}$, which is then affected by ongoing stimuli.
• Fuzzy logic introduces individual-specific variability, so similar situations yield distinct trajectories (Georgeson, 2016).

2.2 Personality Shaping in LLMs and Agents

Modern methods adapt an LLM’s or agent’s personality profile using reinforcement learning, fine-tuning, prompt engineering, or architectural interventions:

• PANDA (Lim et al., 9 Apr 2025) directs an RL agent’s Q-values using a classifier scoring function, e.g.,

$$Q'(s_t, a_t) = Q(s_t, a_t) + \gamma \cdot C(s_t, a_t | p)$$

This biases actions toward the target trait $p$ based on the classifier output $C$.

• In LLMs, Big5-Scaler (Cho et al., 8 Aug 2025) modulates outputs using scalable, numerically-anchored prompts ("Your openness score is {openness}/100"), enabling proportional trait adjustment and real-time switching.
• PsychAdapter (Vu et al., 22 Dec 2024) injects a continuous trait vector across all transformer layers using layer-specific projection matrices, allowing nuanced and persistent conditioning for arbitrary psychological traits during generation.

2.3 Structured Editing and Activation Methods

Personality can be systematically edited by modifying model weights or activations:

• PALETTE (Hwang et al., 17 Feb 2025) adapts factual knowledge-editing techniques to personality traits. Rank-one updates shift model responses to psychological queries (e.g., flipping from "Feeling" to "Thinking" in MBTI) by directly updating internal weight matrices.
• Activation-level methods, such as PAS (Zhu et al., 21 Aug 2024), avoid full retraining by adding directional offsets to attention-head activations using trained probes, optimizing for minimal intervention that achieves the desired trait expression:

$$x_{l+1} = x_l + \sum_h Q_l^h (Att_l^h(P_l^h x_l) + \alpha \sigma_l^h)$$

2.4 Architectural and Dataset Strategies

More sophisticated solutions employ adaptive graph structures and multimodal architectures:

• LL4G (Shen et al., 2 Apr 2025) constructs semantic-structural graphs from text posts, with edge and node updates reflecting evolving relationships and new data, enabling continuous profile adaptation.
• Multi-modal frameworks (An et al., 26 Oct 2024) synchronize visual, audio, and textual cues using timestamped alignment, enabling personality prediction that adapts as new behavior is observed.

3. Evaluation Techniques and Effectiveness

Dynamic personality adjustment is assessed via a range of quantitative and qualitative techniques:

• Application of psychometric inventories (IPIP-NEO, BFI, MBTI, PERS-16/16PF) to agent output or LLM generations (Yu et al., 2023, Chittem et al., 26 Jun 2025, Vu et al., 22 Dec 2024, Zhu et al., 21 Aug 2024, Georgeson, 2016, Cho et al., 8 Aug 2025).
• Novel metrics such as the Personality Shaping Deviation Coefficient (PSDC), defined as the Euclidean distance between desired and manifested trait vectors (Liu et al., 10 Apr 2024).
• Human and LLM-based ratings (e.g., expert classification of trait-specific output, RMSE against human benchmarks) (Vu et al., 22 Dec 2024, Cho et al., 8 Aug 2025).
• Realistic multi-agent dialogue or gaming environments with statistical analysis of outcomes: e.g., game scores stratified by Openness in agent-controlled simulations (Lim et al., 9 Apr 2025).
• Subjective ratings for naturalness, effectiveness, and satisfaction in adaptive dialogue systems (Miyama et al., 2022, Cheng et al., 25 Apr 2025).

Performance benchmarks consistently indicate that dynamic/continuous adjustment (e.g., via SAC, hypernetworks, or mixture-of-experts LoRA) produces finer-grained control, better consistency, and improved psychological realism compared to binary or static methods.

4. Applications and Implications

Dynamic personality adjustment underpins a spectrum of applications:

• Non-player characters in games (ExAI, PANDA) that authentically evolve opinions or voting behaviors (Georgeson, 2016, Lim et al., 9 Apr 2025).
• Adaptive chatbots and dialogue agents, including those with multi-modal capacity (voice, gesture) (Miyama et al., 2022).
• Personalized recommendation and customer service systems, tuned to user-specific personality profiles (An et al., 26 Oct 2024, Zhu et al., 21 Aug 2024).
• Human-robot and human-agent teaming, where robots use dynamic personality, appraisal theory, and episodic memory to optimize interaction (Tang et al., 2 Feb 2025).
• Psychological and clinical applications, where agents simulate or assess rare/personality types beyond standard self-report (e.g., PEDANT for synthetic data generation of atypical traits) (Neuman et al., 2023, Zhang et al., 5 Jul 2025).
• Education and training, as in role-playing or counseling where adaptive agents provide tailored support or simulation (Tang et al., 2 Feb 2025, Liu et al., 10 Apr 2024).

The impact is especially pronounced in domains requiring nuanced, context-sensitive, or emotionally congruent behavior. Adjusting traits dynamically also allows for compositional or multi-trait simulation, facilitating more complex or blended personality profiles within a single agent or across interacting entities.

5. Technical Challenges and Future Directions

While dynamic personality adjustment confers substantial flexibility and realism, it presents several ongoing technical and operational challenges:

• Prompt brittleness and drift: Small variations or multi-turn dialogues can cause the intended personality state to attenuate or shift, complicating sustained behavioral control (Fitz et al., 19 Sep 2025).
• Trait interaction effects: Adjusting a single trait often has ripple effects on others (e.g., increasing Warmth can reduce Distrust), suggesting that model representations encode latent multi-dimensional structures rather than fully orthogonal traits (Chittem et al., 26 Jun 2025).
• Safety and alignment trade-offs: Personality modulation is shown to directly impact both task performance and safety/ethics benchmarks, sometimes decoupling competence from reliability (e.g., low Conscientiousness causing drops in safety-critical settings) (Fitz et al., 19 Sep 2025).
• Scaling to nuanced/rare traits: Dynamic adjustment in architectures such as MoE/LoRA mixtures (Dan et al., 18 Jun 2024), hypernetworks (Liu et al., 10 Apr 2024), and psychometric editing (Hwang et al., 17 Feb 2025) improves trait fidelity but increases system complexity and the risk of unintended trait entanglement or exploitation.
• Measurement accuracy: Evaluating real-world alignment demands robust, context-aware measurement, including continuous self-report calibration, AI-specific personality tests, and adversarial/personality-sensitive benchmark design (Yu et al., 2023, Fitz et al., 19 Sep 2025).

Emerging research directions include the integration of more expressive trait models (e.g., moving beyond OCEAN to 16PF or MBTI), multi-trait and multi-modal induction, real-time and context-aware modulation in conversation, and formal psychometric–machine learning bridges (e.g., item-response theory for AI). There is active exploration of the safety implications, particularly regarding adversarial manipulation of trait profiles, and the design of closed-loop, robust personality controllers.

6. Contextual Significance and Broader Implications

Dynamic personality adjustment represents a fundamental axis for controlling, evaluating, and personalizing AI behavior. Its implementation is increasingly central to the development of trustworthy, safe, and human-aligned interactive systems. By enabling tailored and modular adaptation, these methodologies advance both the realism of simulated agents and the functional alignment of deployed AI. The field continues to evolve rapidly, integrating advances in psychometrics, deep learning, and human-computer interaction, and remains at the intersection of artificial intelligence, computational psychology, and ethical AI research.