The field of Large Language Models (LLMs) is moving towards a deeper understanding of personality and behavioral traits in artificial systems. Recent studies have shown that LLMs can exhibit consistent behavioral tendencies resembling human traits, but also reveal dissociation between self-reports and behavior. The development of frameworks for enhancing LLM agents through psychologically grounded personality conditioning has enabled control over behavior along foundational axes of human psychology. Additionally, novel evaluation methodologies have been introduced to capture nuanced behavioral characteristics and create multi-faceted profiles of LLMs. Noteworthy papers include:

• The Personality Illusion, which challenges assumptions about LLM personality and underscores the need for deeper evaluation in alignment and interpretability.
• Psychologically Enhanced AI Agents, which establishes a foundation for psychologically enhanced AI agents without fine-tuning.
• Behavioral Fingerprinting of Large Language Models, which provides a reproducible and scalable methodology for uncovering deep behavioral differences.
• Personality as a Probe for LLM Evaluation, which positions mechanistic steering as a lightweight alternative to fine-tuning for deployment and interpretability.