Key-value (KV) cache compression has emerged as a critical technique for reducing the memory and latency overhead of autoregressive language models during inference. Prior approaches predominantly rely on query-key attention scores to rank and evict cached tokens, assuming that attention intensity correlates with semantic importance. However, this heuristic overlooks the contribution of value vectors, which directly influence the attention output. In this paper, we propose CurDKV, a novel, value-centric KV compression method that selects keys and values based on leverage scores computed from CUR matrix decomposition. Our approach approximates the dominant subspace of the attention output softmax(QK)V, ensuring that the retained tokens best preserve the model's predictive behavior. Theoretically, we show that attention score approximation does not guarantee output preservation, and demonstrate that CUR-based selection minimizes end-to-end attention reconstruction loss.

Every emotional moment from the Gilgo Beach killer's sentencing: Rex Heuermann's shocking first words... and the chilling exchange that silenced the room Don Trump Jr. says Ted Cruz is'lying through his teeth' as GOP infighting over Iran deal continues to spiral LIZ JONES: The cracks in Harry and Meghan's perfect facade have started to show. It's so obvious he's tiring of her tone-deaf approach... and I predict there's serious trouble in store All my friends are suddenly getting divorced. Mid-life wives share taboo sex confessions about why they really leave... including common position that made one hate her husband: JANA HOCKING Trump says'fools who think I haven't been tough enough on Iran' are'jealous or stupid' after signing widely-criticised deal that includes giving Tehran $300billion Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN The Ring star Daveigh Chase's friends searched for her on LA's Skid Row in months before her shock death at 35 Horrific new videos blow Texas woman's mystery death wide open: Her agonizing'final gasp'... unthinkably vile corpse claims... and sick past of man who saw her last'I can still see you': Princess of Wales shares adorable moment with shy little girl at Royal Ascot Brooklyn Beckham is savaged by fans for yet another'classless' swipe at his estranged family as new DoorDash ad is branded a'giant PR mess' Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Kylie Jenner and Timothee Chalamet put on VERY affectionate display during NYC bike ride... as it's revealed how the relationship has'changed' the actor Luxury fashion tycoon beloved by the stars hangs her head in shame as she's indicted for allegedly exploiting her workers and stealing $50k from their wages NBA star's fiancee breaks her silence after friend, 26, mysteriously dropped dead at her luxury bachelorette party in St Barts Tragedy as 8-year-old dies during World Cup watch party with cops blaming'accidental drowning' Instead, I lost a stone and dropped a dress size in one MONTH with a meal plan that's not even a diet. It's packed with carbs and so simple - anyone can do it in time for summer Gen Z are scared of ringing the DOORBELL: One in three youngsters now text or call when they arrive at someone's door because they think it's less awkward It's something most people do without thinking twice about it. But a new survey has revealed how ringing the doorbell is leaving many Gen Z petrified.

The 2026 World Cup promises a global celebration. Many Arab fans may find themselves excluded. For the first time in World Cup history, eight Arab nations have qualified for this year's tournament, including Morocco, Tunisia, Egypt, Algeria, Saudi Arabia, Qatar, Iraq, and Jordan--double the number of teams that qualified for Qatar in 2022. Yet, the tournament is taking place at an unprecedented moment of heightened geopolitical tension. The US-Israel war with Iran, which began in February of this year, has caused ripple effects across Gulf states and neighboring countries in the Levant, including Lebanon, Palestine, and Jordan, reshaping the security around travel and mobility for fans and players hailing from the region. The US State Department has fully suspended visa issuance for nationals from countries with teams that qualified, including Iran and Haiti--despite it being the first time Haiti has qualified for a World Cup since 1974.

Moscow hit by largest Ukrainian attack since start of Russia's full-scale war Moscow has come under the largest Ukrainian attack since the start of the full-scale war, with close to 200 drones hitting targets around the Russian capital and setting columns of thick smoke billowing high into the sky. Seventeen people were wounded in the Moscow region, according to local governor Andrei Vorobyov. Almost 1,000 drones and four Ukrainian cruise missiles were intercepted and destroyed across the country in 24 hours, Russia's defence ministry was quoted as saying. An oil depot was struck in the southern Rostov region, where one person was killed. Volodymyr Zelensky said Kyiv had once again hit the Moscow region with long-range sanctions - a euphemism for Ukrainian long-distance strikes on Russia.

Russia’s defense ministry said 555 drones were shot down across the country. Sobyanin said 180 were shot down around Moscow alone.

Equivariant networks enhance model efficiency and generalization by embedding symmetry priors into their architectures. However, most existing methods, primarily based on group convolutions and steerable convolutions, face significant limitations when dealing with complex transformation groups, particularly the projective group, which plays a crucial role in vision. In this work, we tackle the challenge by constructing projective equivariant networks based on differential invariants. Using the moving frame method with a carefully selected cross section tailored for multi-dimensional functions, we derive a complete and concise set of second-order fundamental differential invariants of the projective group. We provide a rigorous analysis of the properties and transformation relationships of their underlying components, yielding a further simplified and unified set of fundamental differential invariants, which facilitates both theoretical analysis and practical applications. Building on this foundation, we develop PDINet, the first framework for deep projective equivariant networks, achieving full projective equivariance without discretizing or sampling the group. Empirical results on the projectively transformed STL-10 and Imagenette datasets show that PDINet achieves improvements of 11.39% and 5.66% in accuracy over the respective standard baselines under out-of-distribution settings, demonstrating its strong generalization to complex geometric transformations.

The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge.

Simulating collective decision-making involves more than aggregating individual behaviors; it emerges from dynamic interactions among individuals. While large language models (LLMs) offer strong potential for social simulation, achieving quantitative alignment with real-world data remains a key challenge. To bridge this gap, we propose the Mean-Field LLM (MF-LLM) framework, the first to incorporate mean field theory into LLM-based social simulation. MF-LLM models bidirectional interactions between individuals and the population through an iterative process, generating population signals to guide individual decisions, which in turn update the signals. This interplay produces coherent trajectories of collective behavior. To improve alignment with real-world data, we introduce IB-Tune, a novel fine-tuning method inspired by the Information Bottleneck principle, which retains population signals most predictive of future actions while filtering redundant history. Evaluated on a real-world social dataset, MF-LLM reduces KL divergence to human population distributions by 47% compared to nonmean-field baselines, enabling accurate trend forecasting and effective intervention planning. Generalizing across 7 domains and 4 LLM backbones, MF-LLM provides a scalable, high-fidelity foundation for social simulation.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

We introduce IntrinsiX, a novel method that generates high-quality intrinsic images from text description. In contrast to existing text-to-image models whose outputs contain baked-in scene lighting, our approach predicts physically-based rendering (PBR) maps. This enables the generated outputs to be used for content creation scenarios in core graphics applications that facilitate re-lighting, editing, and texture generation tasks. In order to train our generator, we exploit strong image priors, and pre-train separate models for each PBR material component (albedo, roughness, metallic, normals). We then align these models with a new cross-intrinsic attention formulation that concatenates key and value features in a consistent fashion.