Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed -- creates specific needs for an appropriate response. However, most evaluations, while capturing the model's capability to respond, often ignore who is asking. This gap is especially critical in stigmatized domains such as opioid use disorder (OUD), where accounting for users' contexts is essential to provide accessible, stigma-free responses. We propose CoRUS (COmmunity-driven Roles for User-centric Question Simulation), a framework for simulating role-based questions. Drawing on role theory and posts from an online OUD recovery community (r/OpiatesRecovery), we first build a taxonomy of asker roles -- patients, caregivers, practitioners. Next, we use it to simulate 15,321 questions that embed each role's goals, behaviors, and experiences. Our evaluations show that these questions are both highly believable and comparable to real-world data. When used to evaluate five LLMs, for the same question but differing roles, we find systematic differences: vulnerable roles, such as patients and caregivers, elicit more supportive responses (+17%) and reduced knowledge content (-19%) in comparison to practitioners. Our work demonstrates how implicitly signaling a user's role shapes model responses, and provides a methodology for role-informed evaluation of conversational AI.

Substance use disorders (SUDs) affect millions of people, and relapses are common, requiring multi-session treatments. Access to care is limited, which contributes to the challenge of recovery support. We present \textbf{ChatThero}, an innovative low-cost, multi-session, stressor-aware, and memory-persistent autonomous \emph{language agent} designed to facilitate long-term behavior change and therapeutic support in addiction recovery. Unlike existing work that mostly finetuned large language models (LLMs) on patient-therapist conversation data, ChatThero was trained in a multi-agent simulated environment that mirrors real therapy. We created anonymized patient profiles from recovery communities (e.g., Reddit). We classify patients as \texttt{easy}, \texttt{medium}, and \texttt{difficult}, three scales representing their resistance to recovery. We created an external environment by introducing stressors (e.g., social determinants of health) to simulate real-world situations. We dynamically inject clinically-grounded therapeutic strategies (motivational interview and cognitive behavioral therapy). Our evaluation, conducted by both human (blinded clinicians) and LLM-as-Judge, shows that ChatThero is superior in empathy and clinical relevance. We show that stressor simulation improves robustness of ChatThero. Explicit stressors increase relapse-like setbacks, matching real-world patterns. We evaluate ChatThero with behavioral change metrics. On a 1--5 scale, ChatThero raises \texttt{motivation} by $+1.71$ points (from $2.39$ to $4.10$) and \texttt{confidence} by $+1.67$ points (from $1.52$ to $3.19$), substantially outperforming GPT-5. On \texttt{difficult} patients, ChatThero reaches the success milestone with $26\%$ fewer turns than GPT-5.

A controversial test for Large Language Models concerns the ability to discern possible from impossible language. While some evidence attests to the models' sensitivity to what crosses the limits of grammatically impossible language, this evidence has been contested on the grounds of the soundness of the testing material. We use model-internal representations to tap directly into the way Large Language Models represent the 'grammatical-ungrammatical' distinction. In a novel benchmark, we elicit probabilities from 4 models and compute minimal-pair surprisal differences, juxtaposing probabilities assigned to grammatical sentences to probabilities assigned to (i) lower frequency grammatical sentences, (ii) ungrammatical sentences, (iii) semantically odd sentences, and (iv) pragmatically odd sentences. The prediction is that if string-probabilities can function as proxies for the limits of grammar, the ungrammatical condition will stand out among the conditions that involve linguistic violations, showing a spike in the surprisal rates. Our results do not reveal a unique surprisal signature for ungrammatical prompts, as the semantically and pragmatically odd conditions consistently show higher surprisal. We thus demonstrate that probabilities do not constitute reliable proxies for model-internal representations of syntactic knowledge. Consequently, claims about models being able to distinguish possible from impossible language need verification through a different methodology.

We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.

One of the most pernicious myths about digital technology is that it is somehow weightless or immaterial. Remember all that early talk about the "paperless" office and "frictionless" transactions? And of course, while our personal electronic devices do use some electricity, compared with the washing machine or the dishwasher, it's trivial. Belief in this comforting story, however, might not survive an encounter with Kate Crawford's seminal book, Atlas of AI, or the striking Anatomy of an AI System graphic she composed with Vladan Joler. And it certainly wouldn't survive a visit to a datacentre – one of those enormous metallic sheds housing tens or even hundreds of thousands of servers humming away, consuming massive amounts of electricity and needing lots of water for their cooling systems.

Now that generative AI models can produce photorealistic, fake images of child sexual abuse, regulators and child safety advocates are worried that an already-abhorrent practice will spiral further out of control. But lost in this fear is an uncomfortable possibility--that AI-generated child pornography could actually benefit society in the long run by providing a less harmful alternative to the already-massive market for images of child sexual abuse. The growing consensus among scientists is that pedophilia is biological in nature, and that keeping pedophilic urges at bay can be incredibly difficult. "What turns us on sexually, we don't decide that--we discover that," said psychiatrist Dr. Fred Berlin, director of the Johns Hopkins Sex and Gender Clinic and an expert on paraphilic disorders. "It's not because [pedophiles have] chosen to have these kinds of urges or attractions. They've discovered through no fault of their own that this is the nature of what they're afflicted with in terms of their own sexual makeup … We're talking about not giving into a craving, a craving that is rooted in biology, not unlike somebody who's having a craving for heroin."

At its Search On event today, Google unveiled several new ways to help people more easily find what they're looking for. Some things can be trickier to locate than most, like a particular style of clothing or a certain fragrance. But when it comes to food that makes your mouth and eyes water, Google thinks it can help. Engadget spoke with vice president and general manager of Local Search Yul Kwon to learn how the company believes it can bring people to the dishes they're craving. You might remember him as the winner of Survivor: Cook Islands, but he's also been a management consultant, a law practitioner and the owner of several Red Mango franchise locations in California.

After months of social distancing, it's not surprising that many people have felt starved for human companionship. Now a study from MIT has found that to our brains, the longings we feel during isolation are indeed similar to the food cravings we feel when hungry. After subjects endured one day of total isolation, looking at pictures of people having fun together activated the same brain region that lights up when someone who hasn't eaten all day sees a picture of pasta. "People who are forced to be isolated crave social interactions similarly to the way a hungry person craves food," says cognitive sciences professor Rebecca Saxe, PhD '03, the senior author of the study. "Our finding fits the intuitive idea that positive social interactions are a basic human need."

Valheim is Steam's latest top-selling, out-of-nowhere indie game, and from some angles, it sure looks the part. Depending on what screenshots you stumble upon, you might get some serious PlayStation 1 nostalgia vibes, with characters, animals, and trees that look straight out of the first '90s Tomb Raider game. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. We've seen this before when it comes to Steam Early Access hits, usually because a game maker spends more time on gameplay and depth, not screenshots.

People crave company when socially isolated -- such as amid lockdown -- in almost exactly the same way that a hungry individual longs for food, a study has concluded. US experts found that after ten hours of seclusion, people not only want company but also exhibit increased brain responses to pictures of social interactions. Social interactions are rewarding, the team said -- and related images (like smiling faces or people chatting) also engages the brain's dopamine-based reward system. The findings build on past work that found mice exhibited increased responses in the midbrain dopamine system when being social after a period of isolation. While this had suggested that the midbrain may play a role in feelings of social isolation, it had been unclear exactly whether the same would apply in humans.