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Reanalyzed Hubble data challenges Europa plume claims
Dr. Kurt Retherford: “The new data has made us reconsider the strength of the previous paper’s conclusion regarding water vapor plumes.” [ https://www.labroots.com/trending/space/30560/reanalyzed-hub…e-claims-2](https://www.labroots.com/trending/space/30560/reanalyzed-hub…e-claims-2)
What can the vapor plumes on Jupiter’s moon Europa teach scientists about the small moon’s atmosphere? This is what a recent study published in Astronomy & Astrophysics hopes to address as a team of scientists investigated the origins of Europa’s vapor plumes. This study has the potential to help scientists better understand the geological activity occurring on Europa and how its subsurface ocean could influence the small moon’s fragile and thin atmosphere.
For the study, the researchers analyzed data obtained from NASA’s Hubble Space Telescope in 1999 and between 2012 and 2020 that displayed evidence of water vapor plumes from Europa and a hydrogen exosphere. An exosphere is the uppermost layer of an atmosphere and is where the atmosphere thins out and merges with the vacuum of space.
This study builds on a 2014 study published in Science from some of these same researchers that explored evidence of plume activity at Europa’s south pole. Now, this most recent study used a series of computer models to ascertain the accuracy of past Hubble data and from the 2014 study. In the end, the researchers discovered that while evidence of the hydrogen exosphere was present, evidence of water vapor plumes was not.
Behold the neuron, a complicated cell with a simple mission
Neurons, the uber-connected nerve cells that act as a main switchboard for the brain, are central to some incredibly complicated processes. They make it possible to think, walk, speak, and breathe. They even have built-in backup batteries to use in emergencies.
Yet the way individual neurons go about their business is surprisingly simple, according to a new Yale study.
How simple? Most of them operate entirely like tiny on-off switches.
Scientists Created a Subatomic Particle That Defies Our Understanding of Physics
For decades, every known atomic and nuclear system has relied on at least two fundamental forces working in concert: the strong force binds protons and neutrons inside the nucleus, while electromagnetism holds electrons in orbit around it. Now, an international team of physicists has found the first experimental evidence of a nuclear system bound exclusively by the strong force—confirming a theoretical prediction made twenty years ago and opening a new window onto how matter acquires mass.
Creating a system held together by only one force required a particle with a special property: no electric charge. Ordinary atoms can’t do the job because their components—protons and electrons—are electrically charged, so electromagnetism is always in play. The Standard Model of particle physics, which describes three of the four fundamental forces (the strong force, the weak force, and electromagnetism —gravity isn’t included), predicts that electrically neutral mesons should be able to bind to a nucleus through the strong interaction alone. The eta prime meson (η′) is the ideal test case: it carries no electric charge, so it can’t be bound electromagnetically, and its unusually large mass makes it a uniquely sensitive probe of the strong force’s inner workings.
Generalization Dynamics of LM Pre-training
An AI has a limited amount of “capacity” (brainpower). Early in training, it develops quick, shallow circuits to memorize data because that’s the easiest way to get the right answer. Later, it develops complex circuits for actual reasoning. Because space is limited, these two internal systems are constantly competing for control. Whichever type of data the AI happens to be reading in a specific moment determines which circuit wins the battle.
People typically assume that LMs stably mature from pattern-matching parrots to generalizable intelligence during pre-training. We build a toy eval suite and show this mental model is wrong: throughout pre-training, LMs frequently and suddenly hop between parrot-like and intelligence-like modes, i.e. distinct algorithms implemented by distinct circuits. We call this mode-hopping. Across our suite, LMs can suddenly latch onto memorized or in-context patterns instead of in-context learning, use System 1 instead of System 2 thinking, pick up what sounds true instead of what is true, fail at multi-hop persona QA, out-of-context reasoning, and emergent misalignment — then just as suddenly revert and generalize. Mode-hopping is not explained by standard optimization dynamics: it is locally stable and can not be fixed by checkpoint averaging. We instead think of it as a capacity allocation problem: in a capacity-bounded model, generalizable circuits must compete with the shallow ones learned early in training, and the data in each pre-training window decides which circuits win. Our suite provides a cheap set of pre-training monitors and a new lens on generalization. Building upon our insights, we demonstrate three applications: (i) select intermediate pre-training checkpoints that strongly generalize reasoning and alignment, better than the final pre-or mid-training checkpoints, (ii) select pre-training data that controls and stabilizes generalization dynamics, and (iii) test prior generalization predictors, falsifying the monolithic belief that “simpler solutions generalize better”
Building general AI without generalization is doable but meh. We want an intelligence that learns deep, transferable structure, not a parrot that matches shallow patterns. Real generalization would unblock many today’s key open problems: data-efficient (online) learning, shortcut learning, transfer capabilities from verifiable domains (math, coding) to broader non-verifiable yet economically valuable domains, and maintain a coherent character that truly aligns with human values.
The distinction between parrots and intelligence is computational. Parrots repeat in-context patterns; intelligence infers in-context functions. Parrots encode a persona as bags of disconnected facts and traits; intelligence learns a shared persona representation that connects all. Parrots memorize reasoning steps; intelligence forms general reasoning circuits for entity tracking, backtracking, or even for highly abstract concepts like truth.
AI-generated fake citations are flooding scientific literature across publications, scientists warn
The citations at the end of a research paper should represent a solid foundation of existing knowledge about a particular field, a pool of peer-reviewed sources built over years of research and study. However, with the increasing use of AI and large language models in writing research papers, there’s a growing chance that the citation someone clicks on may not even exist, and that the study, the source, or even the researchers themselves could be entirely fake.
In a recent study posted to the arXiv preprint server, researchers audited millions of papers and found that an estimated 146,900 hallucinated citations were present in research papers hosted on four major scientific repositories— arXiv, bioRxiv, SSRN, and PubMed Central. These numbers were for 2025 alone.
The hallucinated citations were not limited to a handful of bad apples but appeared across many papers, each containing a small number of fake references, pointing to a broader pattern of researchers using AI yet failing to fact-check the output.
How looking through static can help people with a common degenerative disease see better
Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among aging people globally. Around one in seven Australians over the age of 50 have some signs of AMD.
The disease results in blurred and distorted vision, and often loss of function at the center of the eye’s visual field.
The best current treatment involves a series of injections to slow the progression of the disease, but this process can be expensive and difficult with potentially negative long-term effects.