Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events

• URL: http://arxiv.org/abs/2608.06485v1
• Date: Thu, 06 Aug 2026 18:25:58 GMT
• Title: Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events
• Abstract summary: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing.<n>We study event-induced personality change after 11 major life events using the Big Five traits as a psychometric anchor.<n>Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.
• Score: 34.88005801095875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions. A key component of such coherence is personality evolution: agents should undergo plausible, psychology-grounded changes as they experience life events in different contexts. Although prior work shows that LLM personalities can shift under contextual perturbations, how these shifts vary across traits, events, personas, and models remains poorly understood. We study event-induced personality change after 11 major life events, using the Big Five traits as a psychometric anchor and interpreting the resulting trajectories against longitudinal evidence from human personality psychology. Across four diagnostic axes, PC-Agents exhibit measurable trait shifts at similar rates for event-trait pairs with and without documented human change directions. Even when shifts follow the expected direction, their magnitudes usually fall below human effect-size ranges. Gender and cultural-region prompts show little moderating effect, while persona-level dispersion is compressed three- to four-fold relative to human samples. To enable systematic comparison, we introduce BFI-Adapt, a reusable benchmark for scoring the directional fidelity of event-induced personality change, and use it to rank 14 models. A validation suite shows that the measured shifts exceed no-event retest noise, remain stable under independently paraphrased prompts, exhibit limited and model-dependent convergence with scenario-based behavioral choices, and persist across intervening unrelated dialogue. Together, these checks establish the measured trajectories as robust event-conditioned response patterns. Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.

Related papers

• From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs [8.188989044347595]
  Existing studies of personality-related behavior in LLMs have primarily focused on outputs elicited under personality conditioning.<n>We introduce a framework for discovering, controlling, and validating trait-like representations in LLMs.<n>Our findings provide evidence that LLMs contain controllable trait-like representations linking internal states, situational expression, and behavioral outcomes.
  arXiv  Detail & Related papers  (2026-07-29T12:36:47Z)
• How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation [0.0]
  We study how affect propagates among agents that perceive and appraise one another in a multi-agent crowd simulation.<n>The architecture contains no hand-authored mechanism for directly transferring affective state between agents.<n>We show that the system produces emotional contagion dynamics with spatial, temporal, and personality-dependent structure in sparse, small crowds.
  arXiv  Detail & Related papers  (2026-07-27T23:22:17Z)
• Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior [58.538864650160406]
  We run experiments across four behavioral tasks and 11 frontier LLMs.<n>We find that SR-behavior coherence exists but is selective.<n>These findings suggest that coarse personality frameworks may not be the best tools for testing deployment behavior.
  arXiv  Detail & Related papers  (2026-06-10T22:28:53Z)
• HEART-Bench: Do LLM Agents Exhibit Human-like Psychology? [25.237337617299946]
  This paper introduces a novel benchmark to assess whether LLM agents can simulate coherent, human-like psychology.<n>Our benchmark constructs 11 diverse human characters grounded in Big Five personality traits, with each profile deeply integrated with 1,000 structured autobiographical-style episodic memories.<n>By subjecting agents to varying scenarios, the benchmark evaluates whether they can consolidate their innate personality traits and autobiographical memories to make behavioral decisions consistent with their specific psychological profiles.
  arXiv  Detail & Related papers  (2026-05-28T15:08:03Z)
• PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra [84.59328460968872]
  Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning.<n>We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors.<n>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.
  arXiv  Detail & Related papers  (2026-02-17T15:47:58Z)
• HUMANLLM: Benchmarking and Reinforcing LLM Anthropomorphism via Human Cognitive Patterns [59.17423586203706]
  We present HUMANLLM, a framework treating psychological patterns as interacting causal forces.<n>We construct 244 patterns from 12,000 academic papers and synthesize 11,359 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.<n>Our dual-level checklists evaluate both individual pattern fidelity and emergent multi-pattern dynamics, achieving strong human alignment.
  arXiv  Detail & Related papers  (2026-01-15T08:56:53Z)
• Effects of personality steering on cooperative behavior in Large Language Model agents [0.0]
  We study the effects of personality steering on cooperative behavior in large language models (LLMs) using Prisoner's Dilemma games.<n>Our results show that agreeableness is the dominant factor promoting cooperation across all models.<n>Explicit personality information increases cooperation but can also raise vulnerability to exploitation.
  arXiv  Detail & Related papers  (2026-01-08T14:23:45Z)
• PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring [0.9668407688201359]
  PersonaDrift is a benchmark designed to evaluate machine learning and statistical methods for detecting progressive changes in daily communication.<n>The benchmark focuses on two forms of longitudinal change that caregivers highlighted as particularly salient: flattened sentiment and off-topic replies.<n>Preliminary results show that flattened sentiment can often be detected with simple statistical models in users with low baseline variability.
  arXiv  Detail & Related papers  (2025-11-20T15:15:00Z)
• The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
  Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
  arXiv  Detail & Related papers  (2025-09-03T21:27:10Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.