Why the Reflecting Pool Is Full of Algae After Trump's Renovation Warm weather has fueled a bloom that US National Park Service workers are trying to kill using everything from hydrogen peroxide to nanobubbles ahead of July 4 celebrations. On Wednesday morning, workers poured hydrogen peroxide into the Lincoln Memorial Reflecting Pool in Washington, DC. The treatment is the latest attempt by the Interior Department to control an algae bloom that has turned the pool bright green, despite President Donald Trump's costly renovation to make it "American flag blue" in time for the nation's 250th anniversary . Hot temperatures and climate change are among the risk factors that could be driving the outbreak. The Trump administration spent more than $14 million to update the pool ahead of celebrations across the US capital .

Scientific sketches (e.g., models) offer a powerful lens into students' conceptual understanding, yet AI-powered automated assessment of such free-form, visually diverse artifacts remains a critical challenge. Existing solutions often treat sketch evaluation as either an image classification task or monolithic vision-language models, which lack interpretability, pedagogical alignment, and adaptability across cognitive levels. To address these limitations, we present SketchMind, a cognitively grounded, multi-agent framework for evaluating and improving student-drawn scientific sketches. SketchMind introduces Sketch Reasoning Graphs (SRGs), semantic graph representations that embed domain concepts and Bloom's taxonomy-based cognitive labels. The system comprises modular agents responsible for rubric parsing, sketch perception, cognitive alignment, and iterative feedback with sketch modification, enabling personalized and transparent evaluation.

Some of the optical components used in Atom Computing's quantum computer The race to build the first truly useful quantum computer just got more exciting. A quantum computer made from extremely cold atoms has now passed some of the most important milestones towards usefulness, joining a small group of equally able and promising machines. Though there is wide agreement that sufficiently powerful quantum computers would transform our ability to discover new materials and drugs, and break the encryption that underpins the internet, there are many competing ideas about how best to build them. Industry mainstays such as Google and IBM have spent a decade building quantum computers from tiny superconducting circuits, and this approach is currently the front-runner. But an alternate approach that uses electrically neutral ultracold atoms has recently been gaining traction.

Just a week after an AI disproved an 80-year-old conjecture and astonished mathematicians, another conjecture that had stood for half a century has fallen, inspired by the same techniques, but this time written entirely by humans. Last week, an unreleased AI model from OpenAI disproved an important conjecture first posed by Hungarian mathematician Paul Erdős, called the unit distance problem. The puzzle, which Erdős considered his "most striking contribution to geometry" and which many mathematicians had failed to unravel, concerns the number of similar-sized connections you can make between dots arranged on a flat surface. Erdős had set an upper ceiling on this number, which many experts had assumed was correct. But the AI model showed that this number could in fact be much larger, using an obscure trick from algebraic number theory to make complex structures with extremely high dimensions, which could then be used to arrange the dots in a very different arrangement than humans had considered.

Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional adaptation approaches, such as fine-tuning, modify the underlying models for each specific task. In-context learning, however, consistently underperforms task-specific tuning approaches even when presented with the same examples. While most existing approaches (e.g., prompt engineering) focus on the LLM's learned representations to patch this performance gap, our experiments actually reveal that LLM representations contain sufficient information to make good predictions. As such, we focus on the LLM's reasoning abilities and demonstrate that this performance gap exists due to their inability to perform simple probabilistic reasoning tasks. This raises an intriguing question: Are LLMs actually capable of learning how to reason in a task-agnostic manner? We answer this in the affirmative and, as a proof of concept, propose TART which generically improves an LLM's reasoning abilities using a synthetically trained reasoning module.

Are corpse flowers like'Pangy' dangerous? 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 blooming'Amorphophallus titanum,' also known as corpse flower, in Gunung Leuser National Park, North Sumatra Province, Indonesia in January 2025. Breakthroughs, discoveries, and DIY tips sent six days a week. What's big, rare, and smells like literal death?

The colorful explosion of flowers could last through June. Breakthroughs, discoveries, and DIY tips sent six days a week. The driest place on Earth could soon be awash in wildflowers. Death Valley National Park in California is expected to have the best bloom year since 2016. According to the National Park Service, many of their sprouts have not even flowered yet, so the fleeting beauty is just beginning.

However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers.

Why does chocolate turn white? No need to worry--some molecules just moved around. The white splotches on these pieces of chocolate are known as'chocolate bloom.' Breakthroughs, discoveries, and DIY tips sent six days a week. A few years ago, a small baker from the West Coast had a problem. A day or so after baking chocolate chip cookies, the chocolate chips would develop an unpleasant white haze.

Amateur mathematicians are using artificial intelligence chatbots to solve long-standing problems, in a move that has taken professionals by surprise. While the problems in question aren't the most advanced in the mathematical canon, the success of AI models in tackling them shows that their mathematical performance has passed a significant threshold, say researchers, and could fundamentally change the way we do mathematics. The questions being solved by AI originate from Hungarian mathematician Paul Erdős, who was famous for his ability to pose useful but difficult questions during a career that spanned over six decades. "The questions tended to be very simple, but very hard," says Thomas Bloom at the University of Manchester, UK. By his death in 1996, there were more than 1000 of these unsolved Erdős problems, spanning a wide range of mathematical disciplines, from combinatorics (the study of combinations) to number theory.