We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequentially at adaptively chosen inputs, and each query returns a noisy evaluation of the function. The goal is to identify a prescribed number of discontinuities, known as change points, within a target precision $η$ and confidence level $1-δ$, while using as few samples as possible. We propose an adaptive algorithm that first detects intervals likely to contain change points and then refines their locations to precision $η$. We establish non-asymptotic upper bounds on its sample budget, together with corresponding lower bounds. Prior work shows that jump magnitudes alone determine the asymptotic sample complexity as $δ\to 0$. We reveal that this picture is incomplete beyond this regime. We demonstrate, both empirically and theoretically, that for general $δ$ and $η$, the complexity is jointly governed by the jumps and the relative positions of the change points.

Training large language models (LLMs) such as GPT-5 and Qwen-3 (Singh et al., 2025; Yang et al., 2025) on massive text corpora aims at capturing the underlying distribution of natural language. Yet, it remains challenging for the trained model to extrapolate to out-of-distribution or out-of-domain settings beyond the support of its training data. The literature has seen the development of various data perturbation techniques, such as synonym replacement, random insertion, deletion, and swap, that modify training instances into semantically similar variants to effectively expose LLMs to a broader range of inputs and improve their ability to generalize beyond the training data (Feng et al., 2019, 2020; Li et al., 2024; Cen et al., 2026). However, their approach remains grounded in the discrete, word-level augmentation procedures mentioned previously, which may restrict its adaptivity across diverse domains. While discrete perturbations are simple to use, they could be too coarse and hard to refine due to the complexity of natural language (Park et al., 2022; Li et al., 2023). Meanwhile, fixed perturbations apply the same transformations to the data regardless of the contexts, thus failing to generalize appropriately (Ismailov and Asanova, 2025).

Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) to show that assuming that the causal relations are acyclic and invariant across multiple environments (e.g., the way minimum wage affects employment rate is stable across different geographical regions), \textit{only} two auxiliary environments are sufficient to infer the causal graph for arbitrary nonlinear mechanisms. Moreover, we demonstrate that this implies identifiability of the SCM functional mechanisms: as a corollary, we show that \textit{two} auxiliary environments are sufficient to guarantee correct counterfactual inference. We empirically support our theoretical results on synthetic data.

We study best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure. Recent work established a constant gap in the exponential horizon dependence between lower and upper bounds on the number of samples required to identify an approximately optimal policy. Precisely, known lower bounds scale in Ωpe|β|Hq where H is the horizon of the MDP, while the state-of-the-art upper bound achieves at best Ope2|β|Hq (Mortensen and Talebi, 2025) using a generative model. We show that this extra exponential factor can be traced to overly loose concentration control for exponential utilities. To close this open gap, we revisit the analysis of this problem through a forward-model based algorithm building on KL-based exploration bonuses that we adapt to the entropic criterion. The improvement we get is due to two main novel technical innovations. We leverage the smoothness properties of the exponential utility to derive sharper concentration bounds, and we propose a new stopping rule that exploits further this tightness to obtain a sample complexity that matches the lower bound.

Some of the world's biggest and most powerful brands are attempting to be more cute and cuddly. Tech giants Microsoft and Apple are among a wave of businesses who have recently introduced new cartoon character mascots, a tactic experts say is often used to make a brand seem more human and friendly, and to build a stronger connection with customers. Apple's character, a blue and white figure with an outsized head, has become unofficially known as Little Finder Guy. Introduced in March in social media videos to promote a new laptop, it has gained some positive coverage. Microsoft, which years ago shelved its widely-disliked Clippy paperclip virtual assistant, has also unveiled a new cartoon character for its AI assistant Copilot.

For animals, certain colors scream poison. 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. Although largely safe, turbines still pose a danger to some migratory birds. Breakthroughs, discoveries, and DIY tips sent six days a week. Wind turbines are a net positive for a sustainable society, but that doesn't mean they don't have an environmental impact.

The Metropolitan Police has warned that it is preparing for potential violence and hate speech crimes across two protests in London this Saturday. More than 4,000 officers will be drafted in to police the rival events - possibly one of the largest protest deployment in decades - amid fears that far-right demonstrators could clash with pro-Palestine marchers if the two groups are not kept apart. In addition, tens of thousands of football fans are also expected at Wembley Stadium for the FA Cup Final, adding further pressures on the capital's police. Scotland Yard said the risks meant it had to impose the highest degree of control. Measures the Met is planning include the first authorisation of live facial recognition cameras at a demonstration.

Neanderthal'dentists' treated cavities 59,000 years ago A molar points to some sophisticated dental work performed by our extinct cousins. 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 molar tooth found in Chagyrskaya Cave and its macro-features. Breakthroughs, discoveries, and DIY tips sent six days a week. Neanderthals () were once considered to have been extremely primitive and unsophisticated compared to us humans ().

What are Russia's gains from the Iran war? 'We are not losers; we are winners' 'One of the longest' Russian attacks kills at least six people in Ukraine At least six people have been killed and dozens injured in "one of the longest, massive Russian attacks against Ukraine", according to Ukrainian President Volodymyr Zelenskyy, despite renewed claims from the Russian and United States presidents that the war may be nearing an end. Zelenskyy said the barrage began on Wednesday morning and lasted for hours, striking Kyiv, the western city of Lviv near the Polish border and the Black Sea port of Odesa, among other areas. In the southern region of Kherson, Governor Oleksandr Prokudin said a woman was killed when a Russian drone struck a bus in the town of Bilozerka. Another drone attack in the western region of Rivne killed three people and injured four, according to Governor Oleksandr Koval. In the Kharkiv region in northeastern Ukraine, authorities said a 60-year-old man was killed when Russian forces attacked a community near the city of Zolochiv with first-person view drones.

Uta Frith seems remarkably cheerful and content for someone who's spent six decades trying and failing to get to grips with her life's obsession. "Very little has stood the test of time," she tells me as we sit down in her living room in a leafy estate in Harrow-on-the-Hill, London. Around us, high-ceilinged walls papered in a luxurious red print are barely visible between rammed bookshelves, several model brains and a collection of abstract art. Frith has been searching for the mechanisms that underpin the enigmatic condition of autism ever since she first met profoundly autistic children in the late 1960s. "We could identify them intuitively, but not really scientifically - and I have to say that this is, unfortunately, still the case." Still, Frith's influence on our ever-shifting understanding of autism has been monumental.