Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Astronomers watch the birth of one of the universe's most extreme objects for the first time Astronomers have watched the birth of one of the universe's most extreme objects for the very first time - a magnetar comprising the mass of 500,000 Earths inside a sphere measuring just 12 miles across. Magnetars are a type of neutron star, an incredibly dense object mainly made up of tightly packed neutron, which forms from the collapsed core of a massive star during a supernova. What sets magnetars apart from other neutron stars is that they also have the most powerful known magnetic fields in the universe.

Transformer-based Large Language Models (LLMs) are typically pre-trained with a fixed context window size, e.g ., 4K tokens in Touvron et al. [2023a].

The ability to compare objects, scenes, or situations is crucial for effective decision-making and problem-solving in everyday life. For instance, comparing the freshness of apples enables better choices during grocery shopping, while comparing sofa designs helps optimize the aesthetics of our living space. Despite its significance, the comparative capability is largely unexplored in artificial general intelligence (AGI).

Physicists finally know how much faster time moves on the Red Planet. Breakthroughs, discoveries, and DIY tips sent every weekday. Tracking the first astronauts' visit to Mars won't be as simple as watching a clock or marking days off of a calendar. Thanks to relativity, time actually moves faster on the Red Planet than it does here on Earth. For years, scientists have wondered about the exact temporal difference between planets, but physicists at the National Institute of Standards and Technology (NIST) finally have an answer.

Transformer-based Large Language Models (LLMs) are typically pre-trained with a fixed context window size, e.g ., 4K tokens in Touvron et al. [2023a].

The ability to compare objects, scenes, or situations is crucial for effective decision-making and problem-solving in everyday life. For instance, comparing the freshness of apples enables better choices during grocery shopping, while comparing sofa designs helps optimize the aesthetics of our living space. Despite its significance, the comparative capability is largely unexplored in artificial general intelligence (AGI).

This work aims to formalize some of the ways scientific concepts are formed in the process of theoretical physics discovery. Since this may at first seem like a task beyond the scope of the exact sciences (natural and formal sciences), we begin by presenting arguments for why scientific concept formation can be formalized. Then, we introduce type theory as a natural and well-suited framework for this formalization. We formalize what we call "ways of discovering new concepts" including concept distinction, property preservation, and concept change, as cognitive typing rules. Next, we apply these cognitive typing rules to two case studies of conceptual discovery in the history of physics: Einstein's reasoning leading to the impossibility of frozen waves, and his conceptual path to the relativity of time. In these historical episodes, we recast what a physicist might informally call "ways of discovering new scientific concepts" as compositional typing rules built from cognitive typing rules - thus formalizing them as scientific discovery mechanisms. Lastly, we computationally model the type-theoretic reconstruction of Einstein's conceptual path to the relativity of time as a program synthesis task.

Science Physics Particle Physics Einstein's handwritten encyclopedia entry could fetch $200,000 The six-page draft attempted to lay out the Theory of Relativity for a general audience. Breakthroughs, discoveries, and DIY tips sent every weekday. The draft of an encyclopedia article written by Albert Einstein explaining his Theory of Relativity is up for auction . Although, you'll need to brush up on your German to read the original copy. The unsigned, six-page document entitled "The Essence of the Theory of Relativity" is an early version of an entry later translated into English and included in volume XVI of 1948 edition of .

We introduce Einstein Fields, a neural representation that is designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural network weights. By modeling the \emph{metric}, which is the core tensor field of general relativity, Einstein Fields enable the derivation of physical quantities via automatic differentiation. However, unlike conventional neural fields (e.g., signed distance, occupancy, or radiance fields), Einstein Fields are \emph{Neural Tensor Fields} with the key difference that when encoding the spacetime geometry of general relativity into neural field representations, dynamics emerge naturally as a byproduct. Einstein Fields show remarkable potential, including continuum modeling of 4D spacetime, mesh-agnosticity, storage efficiency, derivative accuracy, and ease of use. We address these challenges across several canonical test beds of general relativity and release an open source JAX-based library, paving the way for more scalable and expressive approaches to numerical relativity. Code is made available at https://github.com/AndreiB137/EinFields

Hallucinations are one of the major problems of LLMs, hindering their trustworthiness and deployment to wider use cases. However, most of the research on hallucinations focuses on English data, neglecting the multilingual nature of LLMs. This paper describes our submission to the SemEval-2025 Task-3 - Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. We propose a two-part pipeline that combines retrieval-based fact verification against Wikipedia with a BERT-based system fine-tuned to identify common hallucination patterns. Our system achieves competitive results across all languages, reaching top-10 results in eight languages, including English. Moreover, it supports multiple languages beyond the fourteen covered by the shared task. This multilingual hallucination identifier can help to improve LLM outputs and their usefulness in the future.