The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. Currently, there are no dynamic encoding models that explicitly model a latent state and the entire neuronal response distribution. We address this gap by proposing a probabilistic model that predicts the joint distribution of the neuronal responses from video stimuli and stimulus-independent latent factors. After training and testing our model on mouse V1 neuronal responses, we find that it outperforms video-only models in terms of log-likelihood and achieves improvements in likelihood and correlation when conditioned on responses from other neurons. Furthermore, we find that the learned latent factors strongly correlate with mouse behavior and that they exhibit patterns related to the neurons' position on the visual cortex, although the model was trained without behavior and cortical coordinates. Our findings demonstrate that unsupervised learning of latent factors from population responses can reveal biologically meaningful structure that bridges sensory processing and behavior, without requiring explicit behavioral annotations during training.

The quality of foundation models depends heavily on their training data. Consequently, great efforts have been put into dataset curation. Yet most approaches rely on manual tuning of coarse-grained mixtures of large buckets of data, or filtering by hand-crafted heuristics. An approach that is ultimately more scalable (let alone more satisfying) is to learn which data is actually valuable for training. This type of meta-learning could allow more sophisticated, fine-grained, and effective curation. Our proposed DataRater is an instance of this idea. It estimates the value of training on any particular data point. This is done by meta-learning using'meta-gradients', with the objective of improving training efficiency on held out data. In extensive experiments across a range of model scales and datasets, we find that using our DataRater to filter data is highly effective, resulting in significantly improved compute efficiency.

Trump says algae-infested Reflecting Pool must be EMPTIED for repairs as knife-wielding'vandals' tear hole in facade and destroy $16 million renovation Angelina Jolie's son Pax, 22, surfaces in LA after bombshell revelation about his relationship to Brad Pitt Mortifying truth about Clavicular's'botched' nose job: Infertile influencer's'trans' admission to friends... as insider reveals what's said behind closed doors - and twisted secrets that'll leave fans floored Inside America's new fattest town: Burgers are the size of your head, gyms lie empty and custom mobility scooters carry 800lb loads... as we investigate why Ozempic just DOESN'T work Call me cynical, but the real reason Gruesome Twosome Harry and Meghan are returning to the UK is just so obvious... and highly humiliating: MAUREEN CALLAHAN New York Knicks fan caught in'disgusting' act during team's NBA championship parade celebrations Keir Starmer'will announce as early as Monday that he is quitting as Prime Minister' after spending weekend locked in tense talks about his future with his wife Victoria at Chequers I lost 50lb without jabs using this easy but overlooked method. But I still felt dowdy - until I discovered these expert anti-ageing fashion and beauty tips. No one can see the real reason Jelly Roll divorced Bunnie XO. Former Olympian seen in handcuffs as Trump threatens'years in jail' and more arrests after vandals SABOTAGE Reflecting Pool with'corrosive and destructive chemicals' Giorgia Meloni rips'senseless' attacks from Trump as Italian Prime Minister refuses to back down amid G7 feud Stingy fast food giant named America's favorite restaurant AGAIN... and experts think they know why TV star mom, 46, who appeared on'quitting everything to change your life' show died in fire at luxury Caribbean beach resort that sent 1,700 tourists running for their lives Wyndham Clark's stunning girlfriend pays tribute to polarizing golfer as he stands on the brink of US Open glory Blake Lively runs errands in frumpy outfit after reconciling with ex-BFF Taylor Swift... miles away from reported'bachelorette party' Forget almond, soy, or oat - Britain has gone crazy for unhomogenised milk. New figures released by Waitrose have revealed how sales of the trendy drink have soared by 34 per cent over the last year.

Large language models are increasingly capable of handling long-context inputs, but the memory overhead of key-value (KV) cache remains a major bottleneck for general-purpose deployment. While various compression strategies have been explored, sequence-level compression, which drops the full KV caches for certain tokens, is particularly challenging as it can lead to the loss of important contextual information. To address this, we introduce UniGist, a sequence-level long-context compression framework that efficiently preserves context information by replacing raw tokens with special compression tokens (gists) in a fine-grained manner. We adopt a chunk-free training strategy and design an efficient kernel with a gist shift trick, enabling optimized GPU training. Our scheme also supports flexible inference by allowing the actual removal of compressed tokens, resulting in realtime memory savings. Experiments across multiple long-context tasks demonstrate that UniGist significantly improves compression quality, with especially strong performance in detail-recalling tasks and long-range dependency modeling.

We introduce ReplaceMe, a generalized training-free depth pruning method that effectively replaces transformer blocks with a linear operation, while maintaining high performance for low compression ratios. In contrast to conventional pruning approaches that require additional training or fine-tuning, our approach requires only a small calibration dataset that is used to estimate a linear transformation, which approximates the pruned blocks. The estimated linear mapping can be seamlessly merged with the remaining transformer blocks, eliminating the need for any additional network parameters. Our experiments show that ReplaceMe consistently outperforms other training-free approaches and remains highly competitive with state-of-the-art pruning methods that involve extensive retraining/fine-tuning and architectural modifications. Applied to several large language models (LLMs), ReplaceMe achieves up to 25% pruning while retaining approximately 90% of the original model's performance on open benchmarks--without any training or healing steps, resulting in minimal computational overhead.

Cornell engineers have developed a robotic collective that behaves less like a machine and more like a material that flows, reshapes and adapts to its environment without centralized control. The system, called the Cross-Link Collective, consists of dozens of small robots that have limited mobility individually, but together exhibit coordinated and sustained motion. The research, published May 20 in Science Robotics, demonstrates a robotic system that resembles soft matter, continuously deforming and reorganizing as it moves, driven by what researchers call mechanical intelligence. "Instead of relying on explicit computation and communication, the system shifts the intelligence into the shape of the robots and their physical interactions," said corresponding author Kirstin Petersen, associate professor of electrical and computer engineering and the Aref and Manon Lahham Faculty Fellow in the Cornell Duffield College of Engineering. "We're leveraging the contact dynamics to let useful behaviors emerge, so the system naturally settles into configurations that reduce internal stresses and improve motion."

The quadratic complexity of the attention mechanism in Transformer models has motivated the development of alternative architectures with sub-quadratic scaling, such as state-space models. Among these, Mamba has emerged as a leading architecture, achieving state-of-the-art results across a range of language modeling tasks. However, Mambas performance significantly deteriorates when applied to contexts longer than those seen during pre-training, revealing a sharp sensitivity to context length extension. Through detailed analysis, we attribute this limitation to the out-of-distribution behavior of its state-space dynamics, particularly within the parameterization of the state transition matrix A. Unlike recent works which attribute this sensitivity to the vanished accumulation of discretization time steps, exp( PN t=1 t), we establish a connection between state convergence behavior as the input length approaches infinity and the spectrum of the transition matrix A, offering a well-founded explanation of its role in length extension. Next, to overcome this challenge, we propose an approach that applies spectrum scaling to pre-trained Mamba models to enable robust long-context generalization by selectively modulating the spectrum of Amatrices in each layer. We show that this can significantly improve performance in settings where simply modulating t fails, validating our insights and providing avenues for better length generalization of state-space models with structured transition matrices.

The boom-and-bust cycle has always been a feature of capitalism, and--capturing as it does the human traits of creativity, hope, greed,, anxiety, and panic--it always will be. Creativity gives rise to technological progress and transformative inventions, which provide a new driving force for the economy and a focal point for investors. Today, we are living through another speculative boom. This time the transformative invention is, of course, A.I., and last week's SpaceX I.P.O. While Elon Musk's creation is an impressive rocket-and-satellite company, the stunning $1.78-trillion valuation of the I.P.O. was largely based on its ambitions to build A.I. data centers in space, which remain largely untested .

The great German philosopher, who died in March, understood how much depended on a principled public sphere. Habermas emerged from the uncompromising Frankfurt School, but his work was considerably less fatalistic. You wake up and brace yourself for the barrage of toxic gibberish that constitutes the modern public sphere. Your e-mail is overrun with spam, scams, and smut. There are voice mails from no one about nothing. A glance at the news reveals that the President is continuing to spew lies and obscenities; that a trillionaire is peddling white-supremacist propaganda on a social-media platform he owns; that a chart-topping musical artist is praising Hitler, or apologizing for praising Hitler, or praising Hitler once again. Publications from the on down employ clickbait headlines that treat you like a starving rat in a Pavlovian experiment. A.I. systems simulate the experience of talking to an arrogant ten-year-old boy who knows far less than he thinks he does. When pressed, the chatbots admit that they cannot "naturally understand human morality, dignity, culture, or meaning." It all adds up to a continuous discursive tinnitus--a buzz of random, fake, stupid, sinister chatter that nobody wants and nobody can stop. The person who should have been best able to explain how we got here was the great German philosopher Jürgen Habermas, who illuminated how a feisty, principled public sphere is integral to democracy. But Habermas died in March, at the age of ninety-six, and, although he remained active until his final months, commenting on Ukraine, Gaza, and Eurobonds, he struggled to understand the turn history had taken. As a teen-ager in 1945, he had witnessed American soldiers enter his home town of Gummersbach, near Cologne, carrying messages of freedom and openness. Eight decades later, he watched American voters choose a leader who had advertised his fascistic bent in blood-and-soil rhetoric, fantasies of punitive violence, and a taste for bombastic architectural kitsch.

A new report warns that Miami, Kansas City, Philadelphia, Dallas, and Houston could be particularly hot places to play during the 2026 World Cup. Extreme heat will be one of the biggest challenges for players and fans during the 2026 FIFA World Cup . According to an analysis by the World Weather Attribution (WWA), around 25 percent of the 104 matches of the tournament could be played under temperatures that exceed the recommended thermal safety limits. The study points out that the probability of facing these conditions is almost double that recorded in the 1994 tournament held in the United States. The projections were developed using a statistical model designed to calculate the probability of each match being played in extremely hot conditions.