New research suggests that an enormous amount of chivalric manuscripts disappeared. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Researchers have recreated the evolutionary trees of medieval texts. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Get your news from a source that's not owned and controlled by oligarchs. The trees are now considered invasive, and their bark contributes to wildfire risk. In 2023, author and poet Victoria Chang watched as the massive eucalyptus tree across the street from her home in Los Angeles was cut down. As the men lopped off the tree's limbs, Chang realized she hadn't spent much time really looking at it. She reflected that the tree had probably taken years to grow and was so easily cut down in just a few days. Chang felt compelled to write poems about this feeling that would later evolve into her latest poetry collection, which asks what it means to be human in the face of nature.

We study the Bayesian fixed-budget best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to an abstention budget $α$, we analyze the probability of undetected error--the risk of recommending a suboptimal arm without abstaining. Our central finding is that abstention induces a phase transition: without abstention, the error probability decays polynomially in the sampling budget $T$; in contrast, introducing any small positive abstention budget shifts this to an exponential decay. For Gaussian priors and rewards, in the regime $T\to\infty$ followed by $α\downarrow0$, we establish exact matching information-theoretic lower bounds and algorithmic upper bounds on the optimal error exponent, which takes the form $\exp(-\frac{α^{2}T}{8κ_ν^{2}})$. The hardness parameter $κ_ν$ represents the prior density of the top-two gap at zero, highlighting that nearly tied instances drive the fundamental error. We introduce an adaptive algorithm, PGWS, that successfully achieves this optimal exponent by expending its abstention budget on statistically ambiguous instances. We further demonstrate that this polynomial-to-exponential improvement is exclusively a Bayesian phenomenon--in the frequentist setting, abstention only affects lower-order exponent terms. We also extend our results beyond the Gaussian model.

Lost books by ancient philosophers recovered from'unreadable' scrolls Long-lost works of ancient philosophy have been recovered from papyrus scrolls that were scorched by the AD 79 eruption of Mount Vesuvius and thought to be impossible to read. For the first time, researchers have used AI to extract the entire surviving text from super-high-resolution 3D scans of a scroll without unrolling it. The scrolls come from the library of Herculaneum, which was buried along with Pompeii nearly 2000 years ago. Scholars have been trying to read the carbonised scrolls, which resemble lumps of charcoal, since the library was discovered in 1752. Physically unwrapping them risks their destruction and the ink they are written in is mostly indistinguishable from the charred papyri - at least to human eyes.

Google's first new smart speaker in six years is here and once again leads its competitors--now with paywalled features. Sounds a little more human than competitors. Gemini is helpful and smart. Some assistant features are hidden behind paywalls. Works best if you buy or have bought several Google devices for your home.

In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typically sacrifice one of these goals: deep state space models often assume Gaussian latent transitions, limiting fit and forecasting, while diffusion models are highly expressive but lack principled inference for the underlying dynamics. To combine the strengths of both, we introduce the Diffusion-Driven State Space Model (DDSSM), which replaces the conventional Gaussian transition distribution with a diffusion model. Our DDSSM resolves the open problem of how to jointly train an autoencoder and a diffusion model on sequential data, thereby extending the literature on latent diffusion models for time series. Moreover, we find that the DDSSM empirically outperforms a state-of-the-art deep SSM at fitting and forecasting a simulated time series with multimodal transitions.

Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream tasks of interest can also be difficult. Following first principles decision theory, we demonstrate that BED can alternatively be formulated in terms of an expected future loss (EFL) on downstream actions, providing a simple and naturally task-driven framework. Critically, we then show that all such EFLs can be rearranged into singly intractable objectives that can be jointly optimised with respect to both the design policy and a downstream action policy using stochastic gradients, an approach we refer to as ACTION-BED. This formulation further sidesteps the need for any explicit posterior or marginal likelihood estimation and is naturally implicit, requiring only the ability to sample from the joint model over model parameters and data, and evaluate the downstream loss function. It thus allows design policies to be learned more effectively, efficiently, and simply than existing methods, while providing easy customisation to different downstream tasks and losses.

Clinical reasoning in medicine is a hypothesis-driven process where physicians refine diagnoses from limited information through targeted history, physical examination, and diagnostic investigations. In contrast, current medical benchmarks for large language models (LLMs) primarily assess knowledge recall through single-turn questions, where complete clinical information is provided upfront. To address this gap, we introduce VivaBench, a multi-turn benchmark that evaluates sequential clinical reasoning in LLM agents. Our dataset comprises 1152 physiciancurated clinical vignettes structured as interactive scenarios that simulate a viva voce examination in medical training, requiring agents to actively probe for relevant findings, select appropriate investigations, and synthesize information across multiple steps to reach a diagnosis. We evaluated several state-of-the-art LLMs and found that while models demonstrate competence in diagnosing conditions within well-described clinical presentations, their performance degrades significantly when required to navigate diagnostic uncertainty. Our analysis identified several failure modes that mirror common issues in clinical practice, including: (1) fixation on initial hypotheses, (2) excessive investigation ordering, (3) premature diagnostic closure, and (4) missing critical conditions. These patterns reveal fundamental limitations in how current LLMs manage uncertainty and gather information sequentially. Through VivaBench, we provide a standardized benchmark for evaluating conversational medical AI systems for real-world clinical decision support. Beyond medical applications, we contribute to the larger corpus of research on agentic AI by demonstrating how sequential reasoning trajectories can diverge in complex decision-making environments.

It's a trait evolution forgot to get rid of. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A colorized film still from the 1931 German film'Emil and the Detectives' shows a man sleepwalking. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Mobile kitchens ensured that pizzas arrived piping hot. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. In 1962, Pizza on Wheels aimed to deliver restaurant-fresh pizza straight from the oven. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .