Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set. We ask whether the Markov boundary is genuinely useful for prediction on SCM3K, a 3,450-task synthetic SCM benchmark with feature counts from 40 to 1000 and six SCM families, evaluated with six regressors. The answer is more nuanced than the theory suggests. Restricting a regressor to the oracle boundary often improves prediction substantially, and the improvement grows as the feature space becomes larger and sparser. But the natural pipeline of recovering the boundary with causal discovery and training on the recovered mask does not deliver. Existing estimators exhaust the compute budget before reaching the regime where the boundary helps most, and even where they run they rarely beat the full feature set. We trace this to three causes. Discovery optimizes structural recovery rather than prediction. False negatives and false positives carry sharply asymmetric predictive cost. The exact boundary is only one of many feature sets that beat all features. We then develop what these facts imply for prediction-aligned feature selection and for tabular models that learn to use causal structure.

An undergraduate student first spotted the translucent species off the coast of Taiwan. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. These are some of the ingredients that come together to make, a newly identified species of sea slug, or nudibranch, found swimming in Taiwan. "Taiwanese divers call it'sesame' in Chinese and it is also small like a sesame seed, hence the name," researchers explain in a statement .

Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of established science. We introduce DiscoverPhysics, an interactive benchmark that asks a LLM agent to discover the laws of motion of a simulated world whose physics deliberately deviates from our own. We construct 22 worlds governed by, among others, screened and fractional-power gravity, multi-species couplings, hidden dark-matter-like particles, non-coordinate-free physics, and time-varying interactions. Each world is generated on demand by an N-body simulator, for which the agent proposes several rounds of experiments, observes raw trajectory data, and ultimately submits both a natural-language explanation of the world's physics and a Python implementation of the inferred law. Because solving a world requires the agent to design informative experiments and revise its hypotheses, the benchmark probes long-horizon reasoning over an experimental history. We evaluate submissions along two complementary axes: trajectory MSE on held-out particles and an LLM-judged explanation score following an expert-written rubric assessing conceptual understanding of each world. Across eleven frontier models, we find that the strongest agents pass only half of the worlds and consistently fail on those where latent structure must be uncovered. Open-source models lag substantially behind commercial models, both in their ability to design informative experiments and in extracting conclusions from the data. We further find that good predictive accuracy does not guarantee high explanation quality and that conceptual understanding depends on hypothesis refinement through well-chosen experiments.

Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of case-by-case mathematical derivations. We introduce symbolic density estimation (SDE), an unsupervised framework that automatically recovers closed-form probability mass functions by composing elementary analytic operations within a structured search space. Our method integrates domain-specific structural priors with evolutionary search and a validity-aware inference stage, and it extends to richer distribution families such as zero inflation and finite mixtures. To support systematic evaluation and future research, we contribute a benchmark dataset spanning a broad collection of commonly used discrete distributions. The proposed algorithm recovers all benchmark families with accurate parameter estimates. A real data application shows that it identifies concise and interpretable mixture models that improve goodness-of-fit over standard models.

The poem appears to have geophysics and geology that was not understood in medieval times. 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. "The Divine Comedy" is divided into the "Inferno," "Purgatorio," and "Paradiso." Breakthroughs, discoveries, and DIY tips sent six days a week. Dante Alighieri's is one of the most famous Italian literary works, if not most famous.

From a WWII submarine sewage disaster to a deadly medieval pit toilet collapse, doing your business can come with risks. 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. Toilets can be surprisingly dangerous. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1076, a Dutch nobleman named Duke Godfrey "the Hunchback" of Lower Lorraine was murdered in a most unusual way .

'I knew instantly we had a jackpot.' 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. Both species appear to have a preference for ginger plants. Breakthroughs, discoveries, and DIY tips sent six days a week. The happy-face spider () is famous for the particularly cheery looking patterns on top of its abdomen.

Directed Acyclic Graphs (DAGs) are central to uncovering causal structure in complex systems, yet learning a single DAG from data is often challenging: model uncertainty, finite samples, and a combinatorially large search space frequently yield unstable estimates. We propose DAGgr, a model averaging framework that aggregates multiple candidate DAGs into a single stable representation. Candidate graphs are weighted by their out-of-sample predictive likelihood across repeated data splits, and a thresholding rule on the resulting edge-importance scores guarantees that the aggregated graph is itself acyclic. We establish a finite-sample risk bound, prove that the procedure preserves acyclicity, and show that edge selection is consistent under mild conditions on the weights. Simulations across random, hub, and chain structures, together with an analysis of the Sachs et al. (2005) protein-signaling network, show that DAGgr matches or exceeds the best individual candidate while consistently outperforming bootstrap-aggregation baselines across structural recovery metrics.

The dog survived traumatic injuries, thanks to his Barkindji caretakers. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. The remains of an ancient dingo is shining new light on deep relationships between Australia's First Nations and the wild dogs . Barkindji ancestors deliberately cared for and buried the dingo along the Baaka (Darling River) about 800 miles west of Sydney.

The rise of AI tools that instantly answer questions and complex problems could make humans less intelligent, the Royal Observatory Greenwich has warned. The Observatory, one of the UK's oldest purpose-built scientific institutions, is known for its contributions to astronomy. Paddy Rodgers, director of the Royal Museums Greenwich group which oversees it, said its rich history of research showed the power of human knowledge and curiosity - and the need to avoid complete dependence on AI. A reliance solely on instant answers risks losing the habits of questioning and evaluation that underpin knowledge, expertise and innovation, he said. Rodgers' remarks come amid an ongoing transformation of the Royal Observatory in a project called First Light. The project hopes to seize on the passion of all the astronomers over the last 350 years, and interpret that passion through science, Rodgers told the BBC.