# PTCBENCH: Benchmarking Contextual Stability of Personality Traits in LLM Systems

arXiv: https://arxiv.org/abs/2602.00016 · HTML: https://mlsanity.com/paper/2602.00016/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://mlsanity.com/paper/2411.11581/) (2024)
- [BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data](https://mlsanity.com/paper/2410.16491/) (2024)
- [Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2511.00222/) (2025)
- [CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds](https://mlsanity.com/paper/2412.05631/) (2024)
- [AI Companions Reduce Loneliness](https://mlsanity.com/paper/2407.19096/) (2024)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Evaluating Personality Traits in Large Language Models: Insights from Psychological Questionnaires](https://mlsanity.com/paper/2502.05248/) (2025)
- [LLMs Simulate Big Five Personality Traits: Further Evidence](https://mlsanity.com/paper/2402.01765/) (2024)
- [PersoBench: Benchmarking Personalized Response Generation in Large Language Models](https://mlsanity.com/paper/2410.03198/) (2024)
- [Personality-Driven Decision-Making in LLM-Based Autonomous Agents](https://mlsanity.com/paper/2504.00727/) (2025)
- [Neuron-based Personality Trait Induction in Large Language Models](https://mlsanity.com/paper/2410.12327/) (2024)
- [The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents](https://mlsanity.com/paper/2502.20859/) (2025)
- [Role-Playing Evaluation for Large Language Models](https://mlsanity.com/paper/2505.13157/) (2025)
- [A Comparative Study of Large Language Models and Human Personality Traits](https://mlsanity.com/paper/2505.14845/) (2025)
- [Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues](https://mlsanity.com/paper/2506.15928/) (2025)

## Built on by (most cited first, 4 of 4)

- [Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events](https://mlsanity.com/paper/2608.06485/) (2026)
- [FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents](https://mlsanity.com/paper/2606.31522/) (2026)
- [Identity Is More Than Recall: A Benchmark for Persistent Identity in Deployed AI Agents](https://mlsanity.com/paper/2609.13637/) (2026)
- [Emergi-PersonaOS: A Persona Agent Operating System for Situational Adaptation and Controllable Evolution](https://mlsanity.com/paper/2609.27417/) (2026)

## Abstract (arXiv)

With the increasing deployment of large language models (LLMs) in affective agents and AI systems, maintaining a consistent and authentic LLM personality becomes critical for user trust and engagement. However, existing work overlooks a fundamental psychological consensus that personality traits are dynamic and context-dependent. To bridge this gap, we introduce PTCBENCH, a systematic benchmark designed to quantify the consistency of LLM personalities under controlled situational contexts. PTCBENCH subjects models to 12 distinct external conditions spanning diverse location contexts and life events, and rigorously assesses the personality using the NEO Five-Factor Inventory. Our study on 39,240 personality trait records reveals that certain external scenarios (e.g., "Unemployment") can trigger significant personality changes of LLMs, and even alter their reasoning capabilities. Overall, PTCBENCH establishes an extensible framework for evaluating personality consistency in realistic, evolving environments, offering actionable insights for developing robust and psychologically aligned AI systems.