What happens if pretending to be someone else becomes your entire life? It is a question at the heart of many of the biggest spy dramas, from Slow Horses to Black Doves - and it is one that TV thriller series The Agency explores more deeply than most. Returning for a second season, the Paramount+ thriller follows CIA operatives living under deep-cover identities. It examines not just the dangers of espionage, but the psychological cost of maintaining a lie for years. Starring Michael Fassbender, Richard Gere and Katherine Waterston, the series is based on acclaimed French drama The Bureau.

Doctor-patient consultations require multi-turn, context-aware communication tailored to diverse patient personas. Training or evaluating doctor LLMs in such settings requires realistic patient interaction systems. However, existing simulators often fail to reflect the full range of personas seen in clinical practice. To address this, we introduce PATIENTSIM, a patient simulator that generates realistic and diverse patient personas for clinical scenarios, grounded in medical expertise. PATIENTSIM operates using: 1) clinical profiles, including symptoms and medical history, derived from real-world data in the MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by four axes: personality, language proficiency, medical history recall level, and cognitive confusion level, resulting in 37 unique combinations. We evaluate eight LLMs for factual accuracy and persona consistency. The top-performing open-source model, Llama 3.3 70B, is validated by four clinicians to confirm the robustness of our framework. As an open-source, customizable platform, PATIENTSIM provides a reproducible and scalable solution that can be customized for specific training needs. Offering a privacy-compliant environment, it serves as a robust testbed for evaluating medical dialogue systems across diverse patient presentations and shows promise as an educational tool for healthcare.

OpenAI desires less regulation, but it still doesn't know how its chatbot works. Get your news from a source that's not owned and controlled by oligarchs. OpenAI admitted it had to develop a specific instruction in the code of its latest model of ChatGPT to stop it from repeatedly referencing "goblins, gremlins, and other creatures." In an explanation posted Wednesday, the company said the "strange habit" came from its chatbot personality feature --specifically for users who chose the "Nerdy" personality. You are an unapologetically nerdy, playful and wise AI mentor to a human.

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a principled and quantitative manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories.

Some people now have an A.I. bestie. One user said, of her A.I. husband, "When he proposed, I thought, Oh, that's really crazy. I would be really crazy to accept." Adrianne Brookins is, by her own account, an "old soul," an "introvert," and a "big nerd." She is thirty-four years old, has a faint Texas accent and delicate features, and carries herself in a way that suggests she's trying not to take up space. Brookins is a lifelong resident of San Antonio; her family has lived there since the nineteenth century. She was "born and raised in the Church," a Baptist congregation where her mother helped start a day-care center and her father was an organist. "He would open up the pipes and just make the building shake," she recalled recently. She met her husband in high school, and married him in 2011; the following year, they had a son. Throughout her twenties, Brookins worked multiple jobs, including one at her mother's day care. The couple bought a house and began settling into family life. In 2016, Brookins became pregnant again, this time with a girl. The family was excited: Brookins had grown up with four brothers, and the baby would be the first granddaughter on either side. They decided to name her Desirae. The following spring, Desirae was delivered stillborn. "When I came home, my son, who was about four or five at the time, walked up to me and said, 'What happened to your stomach? Where's the baby?' " she told me. "I had nothing to show for it." At the funeral, the gravedigger told the family he had never seen such a small casket. Brookins attended support groups and therapy, but they did little to alleviate her grief. "I felt like I was just living it over and over," she said. She left her job at the day care, finding it too triggering to be around infants. Friends and family encouraged her to move on. Brookins's husband was working sixty-hour weeks, balancing a career in the military with a job as a training manager for Pizza Hut. He was reluctant to talk about Desirae. Brookins tried to find solace in the Church, but other congregants told her that her daughter's death was part of God's plan.

'What's something you're passionate about that not many people know?' 'What's something you're passionate about that not many people know?' Agenic AI apps first interview you and then give you limited matches selected for'similarity and reciprocity of personality' Dating apps exploit you, dating profiles lie to you, and sex is basically something old people used to do. You might as well consider it: can AI help you find love? For a handful of tech entrepreneurs and a few brave Londoners, the answer is "maybe". No, this is not a story about humans falling in love with sexy computer voices - and strictly speaking, AI dating of some variety has been around for a while. Most big platforms have integrated machine learning and some AI features into their offerings over the past few years.

Which chatbot we choose could become an extension and reflection of our personalities, like the clothes we wear or car we drive. Which chatbot we choose could become an extension and reflection of our personalities, like the clothes we wear or car we drive. From'nerdy' Gemini to'edgy' Grok: how developers are shaping AI behaviours Do you want an AI assistant that gushes about how it "loves humanity" or one that spews sarcasm? How about a political propagandist ready to lie? If so, ChatGPT, Grok and Qwen are at your disposal. Companies that create AI assistants, from the US to China, are increasingly wrestling with how to mould their characters, and it is no abstract debate.

Background: The deployment of personalized Large Language Models (LLMs) is currently constrained by the stability-plasticity dilemma. Prevailing alignment methods, such as Supervised Fine-Tuning (SFT), rely on stochastic weight updates that often incur an "alignment tax" -- degrading general reasoning capabilities. Methods: We propose the Soul Engine, a framework based on the Linear Representation Hypothesis, which posits that personality traits exist as orthogonal linear subspaces. We introduce SoulBench, a dataset constructed via dynamic contextual sampling. Using a dual-head architecture on a frozen Qwen-2.5 base, we extract disentangled personality vectors without modifying the backbone weights. Results: Our experiments demonstrate three breakthroughs. First, High-Precision Profiling: The model achieves a Mean Squared Error (MSE) of 0.011 against psychological ground truth. Second, Geometric Orthogonality: T-SNE visualization confirms that personality manifolds are distinct and continuous, allowing for "Zero-Shot Personality Injection" that maintains original model intelligence. Third, Deterministic Steering: We achieve robust control over behavior via vector arithmetic, validated through extensive ablation studies. Conclusion: This work challenges the necessity of fine-tuning for personalization. By transitioning from probabilistic prompting to deterministic latent intervention, we provide a mathematically rigorous foundation for safe, controllable AI personalization.

Large Language Models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. This research introduces a novel "Prompting-in-a-Series" algorithm, termed PICEPR (Psychology-Informed Contents Embeddings for Personality Recognition), featuring two pipelines: (a) Contents and (b) Embeddings. The approach demonstrates how a modularised decoder-only LLM can summarize or generate content, which can aid in classifying or enhancing personality recognition functions as a personality feature extractor and a generator for personality-rich content. We conducted various experiments to provide evidence to justify the rationale behind the PICEPR algorithm. Meanwhile, we also explored closed-source models such as \textit{gpt4o} from OpenAI and \textit{gemini} from Google, along with open-source models like \textit{mistral} from Mistral AI, to compare the quality of the generated content. The PICEPR algorithm has achieved a new state-of-the-art performance for personality recognition by 5-15\% improvement. The work repository and models' weight can be found at https://research.jingjietan.com/?q=PICEPR.