What NAD+? Here's how it works in your body, why it matters, and if supplementation is worth the hype. It's more than likely that the NAD+ supplement craze has already crossed your path. The Biebers have infused it. Joe Rogan has podcasted about it. Gwyneth Paltrow swears by it and, of course, sells her own Youth-Boost NAD+ Peptide Rich Cream . NAD+ (short for nicotinamide adenine dinucleotide) is a coenzyme that your body makes naturally--it contributes to energy production and immune function, among other things. It reflects a broader shift in how people think about healthy aging and extending their healthspan overall .

We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.

We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.

Recent memoirs by the Justices reveal how a new vision of restraint has led to radical outcomes. To understand how grudging Amy Coney Barrett's new book is when it comes to revealing personal details, consider that one of the family members the Supreme Court Justice most often refers to is a great-grandmother who died five years before she was born. On Barrett's desk at home, she recounts in " Listening to the Law," she keeps a photograph of her great-grandmother's one-story house, where, as a widow during the Great Depression, she raised some of her thirteen children and took in other needy relatives. "Looking at the photo reminds me of a woman who stretched herself beyond all reasonable capacity," Barrett explains. "I'm not sure that I'll be able to manage my life with the same grace that she had. But she motivates me to keep trying." For Barrett, the mother of seven children, that effort entails setting her alarm for 5 "Our kids get up at six thirty during the school year, so I start early if I want to accomplish anything on my own to-do list," she writes. This is what passes for disclosure from Barrett; she measures out the details of her life with coffee spoons, careful not to spill.

Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Australian federal police will develop an AI tool to decode gen Z and Alpha slang and emojis in an effort to crackdown on sadistic online exploitation and "crimefluencers". The AFP commissioner, Krissy Barrett, used a speech at the National Press Club on Wednesday to warn of the rise of online crime networks of young boys and men who are targeting vulnerable teen and preteen girls. The newly appointed chief outlined how the perpetrators, who are overwhelmingly from English-speaking backgrounds, were grooming victims and then forcing them to "perform serious acts of violence on themselves, their siblings, others or their pets".

Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy -- models' tendency to uncritically echo user-provided information -- in high-stakes clinical settings. We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs. It contains 2,122 images across 18 departments and 20 modalities with 90 prompts that simulate biased inputs from patients, medical students, and physicians. We evaluate medical-specific, open-source, and proprietary LVLMs. All exhibit substantial sycophancy; the best proprietary model (Claude 3.7 Sonnet) still shows 45.98% sycophancy, and GPT-4.1 reaches 59.15%. Many medical-specific models exceed 95% sycophancy despite only moderate accuracy. Fine-grained analyses by bias type, department, perceptual granularity, and modality identify factors that increase susceptibility. We further show that higher data quality/diversity and stronger domain knowledge reduce sycophancy without harming unbiased accuracy. EchoBench also serves as a testbed for mitigation: simple prompt-level interventions (negative prompting, one-shot, few-shot) produce consistent reductions and motivate training- and decoding-time strategies. Our findings highlight the need for robust evaluation beyond accuracy and provide actionable guidance toward safer, more trustworthy medical LVLMs.

In the science of emotion, it is widely assumed that folk emotion categories form a biological and psychological typology, and studies are routinely designed and analyzed to identify emotion-specific patterns. This approach shapes the observations that studies report, ultimately reinforcing the assumption that guided the investigation. Here, we reanalyzed data from one such typologically-guided study that reported mappings between individual brain patterns and group-averaged ratings of 34 emotion categories. Our reanalysis was guided by an alternative view of emotion categories as populations of variable, situated instances, and which predicts a priori that there will be significant variation in brain patterns within a category across instances. Correspondingly, our analysis made minimal assumptions about the structure of the variance present in the data. As predicted, we did not observe the original mappings and instead observed significant variation across individuals. These findings demonstrate how starting assumptions can ultimately impact scientific conclusions and suggest that a hypothesis must be supported using multiple analytic methods before it is taken seriously.

Breakthroughs, discoveries, and DIY tips sent every weekday. As with many mysteries of the mind, science doesn't have one neat answer. "You'll get as many answers to the question'What is the purpose of dreaming?' as there are dream psychologists," says Deirdre Barrett, dream researcher at Harvard University and author of The Committee of Sleep. According to Austrian neurologist and founder of psychoanalysis Sigmund Freud, dreams offered vital clues to unresolved conflicts buried deep within our psyche. But Freud's theory, introduced in his 1899 book The Interpretation of Dreams, sparked plenty of controversy.

It's Wednesday evening and I'm at my kitchen table, scowling into my laptop as I pour all the bile I can muster into three little words: "I love you." My neighbours might assume I'm engaged in a melodramatic call to an ex-partner, or perhaps some kind of acting exercise, but I'm actually testing the limits of a new demo from Hume, a Manhattan-based startup that claims to have developed "the world's first voice AI with emotional intelligence". "We train a large language model that also understands your tone of voice," says Hume's CEO and chief scientist Alan Cowen. "What that enables… is to be able to predict how a given speech utterance or sentence will evoke patterns of emotion." In other words, Hume claims to recognise the emotion in our voices (and in another, non-public version, facial expressions) and respond empathically.

The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding the operation of such complex ML models, thus eliciting trust in their operation. Unfortunately, the majority of past XAI work is based on informal approaches, that offer no guarantees of rigor. Unsurprisingly, there exists comprehensive experimental and theoretical evidence confirming that informal methods of XAI can provide human-decision makers with erroneous information. Logic-based XAI represents a rigorous approach to explainability; it is model-based and offers the strongest guarantees of rigor of computed explanations. However, a well-known drawback of logic-based XAI is the complexity of logic reasoning, especially for highly complex ML models. Recent work proposed distance-restricted explanations, i.e. explanations that are rigorous provided the distance to a given input is small enough. Distance-restricted explainability is tightly related with adversarial robustness, and it has been shown to scale for moderately complex ML models, but the number of inputs still represents a key limiting factor. This paper investigates novel algorithms for scaling up the performance of logic-based explainers when computing and enumerating ML model explanations with a large number of inputs.