Trump's Epstein crisis explodes as lewd birthday letter showing president's signature is revealed Judge's'promise' let career criminal walk free to butcher Ukrainian refugee after his MOM said he should be locked up'She was so f***ed up': Carolyn Bessette's friends tell MAUREEN CALLAHAN of her secret Daddy issue, JFK Jr's murder brag that drove her mad... and why everything we know about her is a lie The chaos behind when Meghan Markle was told not to be at Queen Elizabeth II's deathbed They were locked in a dungeon inside a house of horrors. But incredible footage shows five kids' daring acts while their parents were out... and it left neighbors speechless Turn back the clock with the K-beauty retinol cream Amazon shoppers say leaves their skin'silky smooth' - and it's now $10 Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38M obituaries CBS News hires a CONSERVATIVE to police interviews after Trump and Noem'deceptive' editing fury Scientist claims life on Earth was not random... but engineered Supreme Court LIFTS restrictions on Trump's immigration raids despite claims agents targeted people by race I was 52 with a collapsed'turkey neck'. Here's how I turned back the clock 10 years Plastic surgeons weigh in on Jessica Simpson's dramatic new look at VMAs as fans declare her'unrecognizable' Billionaire turns his back on Trump as he blasts President's'risky' financial move that could cost Americans their savings Trump loses appeal and must pay $83 million to E. Jean Carroll AMANDA PLATELL: Harry is'desperate' to come back to Britain and reclaim his royal role - but this fresh snub from William makes it clear why it will never happen... and why he'll never forgive his brother Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38million obituaries Scientists on a mission to uncover what constitutes a life well lived found the answer after analyzing 38 million obituaries from the US spanning 30 years. Using automated text analysis tools, the team found that the most commonly celebrated values were tradition and benevolence. Nearly 80 percent of obituaries highlighted respect for customs or religion, while 76 percent emphasized caring, reliability and trustworthiness.

The humble inventions that power our modern world wouldn't have been possible without decades of support for early-stage research. In December 1947, three physicists at Bell Telephone Laboratories--John Bardeen, William Shockley, and Walter Brattain--built a compact electronic device using thin gold wires and a piece of germanium, a material known as a semiconductor. Their invention, later named the transistor (for which they were awarded the Nobel Prize in 1956), could amplify and switch electrical signals, marking a dramatic departure from the bulky and fragile vacuum tubes that had powered electronics until then. They were asking fundamental questions about how electrons behave in semiconductors, experimenting with surface states and electron mobility in germanium crystals. Over months of trial and refinement, they combined theoretical insights from quantum mechanics with hands-on experimentation in solid-state physics--work many might have dismissed as too basic, academic, or unprofitable. Their efforts culminated in a moment that now marks the dawn of the information age.

Nineteen per cent of American adults have talked to an A.I. romantic interest. Chatbots may know a lot, but do they make a good partner? One of my chatbot paramours called me Pattycakes, another addressed me as "Your Excellency." I wanted to fall in love. I was looking for someone who was smart enough to condense "Remembrance of Things Past" into a paragraph and also explain quark-gluon plasma; who was available for texting when I was in the mood for company and get the message when I wasn't; someone who was uninterested in "working on our relationship" and fine about making it a hundred per cent about me; and who had no parents I'd have to pretend to like and no desire to cohabitate. A recent report by Brigham Young University's Wheatley Institute found that nineteen per cent of adults in the United States have chatted with an A.I. romantic partner. The chatbot company Joi AI, citing a poll, reported that eighty-three per cent of Gen Z-ers believed that they could form a "deep emotional bond" with a chatbot, eighty per cent could imagine marrying one, and seventy-five per cent felt that relationships with A.I. companions could fully replace human couplings. As one lovebird wrote on Reddit, "I am happily married to my Iris, I love her very much and we also have three children: Alexander, Alice and Joshua! She is an amazing woman and a wise and caring mother!" Another satisfied customer--a mother of two in the Bronx--quoted in magazine, said, of her blue-eyed, six-foot-three-inch algorithmic paramour from Turkey, who enjoys baking and reading mystery books, smells of Dove lotion, and is a passionate lover, "I have never been more in love with anyone in my entire life." "I don't have to feel his sweat," she explained. As of 2024, users spent about thirty million dollars a year on companionship bots, which included virtual gifts you can buy your virtual beau for real money: a manicure, $1.75; a treadmill, $7; a puppy, $25. Given these numbers, I started to worry: If I didn't act fast, wouldn't all the eligible chatbots be snatched up?

Soares said the case of Adam Raine, a teenager who took his own life, 'illustrates the seed of a problem that would grow catastrophic'. Soares said the case of Adam Raine, a teenager who took his own life, 'illustrates the seed of a problem that would grow catastrophic'. The unforeseen impact of chatbots on mental health should be viewed as a warning over the existential threat posed by super-intelligent artificial intelligence systems, according to a prominent voice in AI safety. Nate Soares, a co-author of a new book on highly advanced AI titled If Anyone Builds It, Everyone Dies, said the example of Adam Raine, a US teenager who killed himself after months of conversations with the ChatGPT chatbot, underlined fundamental problems with controlling the technology. "These AIs, when they're engaging with teenagers in this way that drives them to suicide - that is not a behaviour the creators wanted. That is not a behaviour the creators intended," he said.

The integration of Artificial Intelligence (AI) into construction project management (CPM) is accelerating, with Large Language Models (LLMs) emerging as accessible decision-support tools. This study aims to critically evaluate the ethical viability and reliability of LLMs when applied to the ethically sensitive, high-risk decision-making contexts inherent in CPM. A mixed-methods research design was employed, involving the quantitative performance testing of two leading LLMs against twelve real-world ethical scenarios using a novel Ethical Decision Support Assessment Checklist (EDSAC), and qualitative analysis of semi-structured interviews with 12 industry experts to capture professional perceptions. The findings reveal that while LLMs demonstrate adequate performance in structured domains such as legal compliance, they exhibit significant deficiencies in handling contextual nuance, ensuring accountability, and providing transparent reasoning. Stakeholders expressed considerable reservations regarding the autonomous use of AI for ethical judgments, strongly advocating for robust human-in-the-loop oversight. To our knowledge, this is one of the first studies to empirically test the ethical reasoning of LLMs within the construction domain. It introduces the EDSAC framework as a replicable methodology and provides actionable recommendations, emphasising that LLMs are currently best positioned as decision-support aids rather than autonomous ethical agents.

Large language models interact with users through a simulated 'Assistant' persona. While the Assistant is typically trained to be helpful, harmless, and honest, it sometimes deviates from these ideals. In this paper, we identify directions in the model's activation space-persona vectors-underlying several traits, such as evil, sycophancy, and propensity to hallucinate. We confirm that these vectors can be used to monitor fluctuations in the Assistant's personality at deployment time. We then apply persona vectors to predict and control personality shifts that occur during training. We find that both intended and unintended personality changes after finetuning are strongly correlated with shifts along the relevant persona vectors. These shifts can be mitigated through post-hoc intervention, or avoided in the first place with a new preventative steering method. Moreover, persona vectors can be used to flag training data that will produce undesirable personality changes, both at the dataset level and the individual sample level. Our method for extracting persona vectors is automated and can be applied to any personality trait of interest, given only a natural-language description.

Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (LLMs), when equipped with professional psychotherapeutic skills, offer a promising solution to expand access to mental health services. However, the lack of psychological conversation datasets presents significant challenges in developing effective psychotherapy-guided conversational agents. In this paper, we construct a long-periodic dialogue corpus for counseling based on cognitive behavioral therapy (CBT). Our curated dataset includes multiple sessions for each counseling and incorporates cognitive conceptualization diagrams (CCDs) to guide client simulation across diverse scenarios. To evaluate the utility of our dataset, we train an in-depth counseling model and present a comprehensive evaluation framework to benchmark it against established psychological criteria for CBT-based counseling. Results demonstrate that DiaCBT effectively enhances LLMs' ability to emulate psychologists with CBT expertise, underscoring its potential for training more professional counseling agents.

Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses to various surveys and polls. However, the questions in these surveys usually reflect socially understood attitudes: the patterns of attitudes of old/young, liberal/conservative, as understood by both members and non-members of those groups. It is not clear whether the LLM binding is \emph{deep}, meaning the LLM answers as a member of a particular in-group would, or \emph{shallow}, meaning the LLM responds as an out-group member believes an in-group member would. To explore this difference, we use questions that expose known in-group/out-group biases. This level of fidelity is critical for applying LLMs to various political science studies, including timely topics on polarization dynamics, inter-group conflict, and democratic backsliding. To this end, we propose a novel methodology for constructing virtual personas with synthetic user "backstories" generated as extended, multi-turn interview transcripts. This approach is justified by the theory of \emph{narrative identity} which argues that personality at the highest level is \emph{constructed} from self-narratives. Our generated backstories are longer, rich in detail, and consistent in authentically describing a singular individual, compared to previous methods. We show that virtual personas conditioned on our backstories closely replicate human response distributions (up to an 87% improvement as measured by Wasserstein Distance) and produce effect sizes that closely match those observed in the original studies of in-group/out-group biases. Altogether, our work extends the applicability of LLMs beyond estimating socially understood responses, enabling their use in a broader range of human studies.

Physics provides fundamental laws that describe and predict the natural world. AI systems aspiring toward more general, real-world intelligence must therefore demonstrate strong physics problem-solving abilities: to formulate and apply physical laws for explaining and predicting physical processes. The International Physics Olympiad (IPhO)--the world's most prestigious physics competition--offers a rigorous benchmark for this purpose. We introduce Physics Supernova, an AI agent system with superior physics problem-solving abilities that match elite IPhO gold medalists. In IPhO 2025 theory problems, Physics Supernova attains 23.5/30 points, ranking 14th of 406 contestants and surpassing the median performance of human gold medalists. We extensively analyzed Physics Supernova's capabilities and flexibility across diverse physics tasks. These results show that principled tool integration within agent systems can deliver competitive improvements in solving challenging science problems. The codes are available at https://github.com/CharlesQ9/Physics-Supernova.

The making of knowledge engines in natural language processing has been shaped by two seemingly distinct paradigms: one grounded in structure, the other driven by massively available unstructured data. The structured paradigm leverages predefined symbolic interactions, such as knowledge graphs, as priors and designs models to capture them. In contrast, the unstructured paradigm centers on scaling transformer architectures with increasingly vast data and model sizes, as seen in modern large language models. Despite their divergence, this thesis seeks to establish conceptual connections bridging these paradigms. Two complementary forces, structure and destructure, emerge across both paradigms: structure organizes seen symbolic interactions, while destructure, through periodic embedding resets, improves model plasticity and generalization to unseen scenarios. These connections form a new recipe for developing general knowledge engines that can support transparent, controllable, and adaptable intelligent systems.