Record 12062026 · captured 2026-08-25
The world looked up 2026 FIFA World Cup. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Ogugua "OG" Anunoby Jr. is a British professional basketball player for the New York Knicks of the National Basketball Association (NBA). He played college basketball for the Indiana Hoosiers and was selected by the Toronto Raptors in the first round of the 20
Julián Andrés Quiñones Quiñones is a professional footballer who plays as a forward or winger for Saudi Pro League club Al-Qadsiah. Born in Colombia, he represents the Mexico national team.
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Jalen Marquis Brunson, nicknamed "Captain Clutch", and the "King of New York" is an American professional basketball player for the New York Knicks of the National Basketball Association (NBA). The son of former NBA guard Rick Brunson, he played college basket
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
Raúl Alonso Jiménez Rodríguez is a Mexican professional footballer who plays as a striker for EFL Championship club Wolverhampton Wanderers and the Mexico national team.
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
On April 2, 2025, Austin Metcalf, a 17-year-old student at Memorial High School, was murdered by Karmelo Anthony, a Centennial High School student of the same age, while attending a school track meet in Frisco, Texas, United States. Anthony stabbed Metcalf aft
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Disclosure Day is a 2026 American science fiction thriller film directed and produced by Steven Spielberg from a screenplay by David Koepp, based on a story by Spielberg. The film stars an ensemble cast, including Emily Blunt, Josh O'Connor, Colin Firth, Eve H
Estadio Azteca, officially known as Estadio Banorte for sponsorship reasons, is a football stadium located in Coyoacán, Mexico City. At an elevation of 2,241 meters (7,352 ft) above sea level it is the official home of Club América of Liga MX and the Mexico na
2026 Peruvian general election
General elections were held in Peru from 12 to 13 April 2026 to elect the president, vice presidents, and the Congress of the Republic of Peru. As no presidential candidate achieved a majority of votes in the first round, a runoff election was held on 7 June.
Chinnasaamy Periyamaya, better known by his stage name Bharathiraja, was an Indian film director, producer, screenwriter and actor who worked mainly in the Tamil film industry. Making his debut in 1977 with 16 Vayathinile, he was known for realistic and sensit
The Mexico national football team represents Mexico in men's international football, which is governed by the Mexican Football Federation founded in 1927. It has been an affiliate member of FIFA since 1929 and a founding affiliate member of CONCACAF since 1961
The New York Knickerbockers, commonly called the New York Knicks, are an American professional basketball team based in the New York City borough of Manhattan. The Knicks compete in the National Basketball Association (NBA) as a member of the Atlantic Division
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
The 2030 FIFA World Cup is scheduled to be the 24th FIFA World Cup, the quadrennial international football tournament that is contested by the men's national teams of the member associations of FIFA. The tournament is planned to be jointly hosted by Morocco, P
Taylor Alison Swift is an American singer-songwriter. An influential figure in popular culture, Swift is known for her autobiographical songwriting and artistic reinventions. She is the highest-grossing live music artist, the wealthiest female musician, and on
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
Brian Gutiérrez is a professional soccer player who plays as an attacking midfielder for Liga MX club Guadalajara. Born in the United States, he represents the Mexico national team.
'Geheimrat Dr. Oldenburg' is a German apple cultivar. It was created in 1897 at the Höheren Lehranstalt für Obstbau of Geisenheim in the Rheingau, in Hesse in central Germany. It may also be known as 'Geheimrat Doktor Oldenburg', 'Geheimrat Oldenburg', or simp
The 2022 FIFA World Cup was the 22nd FIFA World Cup, the quadrennial world championship for national football teams organised by FIFA. It took place in Qatar from 20 November to 18 December 2022, after the country was awarded the hosting rights in 2010. It was
The FIFA Men's World Ranking is a ranking system for men's national teams in association football, first introduced in December 1992. The men's teams of the member nations of FIFA, football's world governing body, are ranked based on their game results with th
South Africa national soccer team
The South Africa national soccer team represents South Africa in men's international soccer and is run by the South African Football Association, the governing body for soccer in South Africa. Nicknamed Bafana Bafana, the team plays at various stadiums around
Shakira Isabel Mebarak Ripoll, known mononymously as Shakira, is a Colombian singer-songwriter, dancer, and record producer. Referred to as the "Queen of Latin Music", she has had a significant impact on the musical landscape of Latin America and has been cred
Francisco Guillermo Ochoa Magaña, commonly known as Memo Ochoa, is a Mexican former professional footballer who played as a goalkeeper. He was a full international with the Mexico national team, which he captained, and is widely considered to be one of the gre
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Karl-Anthony Towns Jr., also known by his initials KAT, is a Dominican American professional basketball player for the New York Knicks of the National Basketball Association (NBA). He was named to the Dominican Republic national team as a 16-year-old and playe
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
full-FORCE: A Target-Based Method for Training Recurrent Networks
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it e
Synchronization of Tree Parity Machines using non-binary input vectors
Neural cryptography is the application of artificial neural networks in the subject of cryptography. The functionality of this solution is based on a tree parity machine. It uses artificial neural networks to perform secure key exchange between network entities. This article proposes improvements to the synchronization of two tree parity machines. The improvement is based on learning artificial neural network using i
This paper develops an Operator Learning framework for approximating the dynamic response of synchronous generators. The framework can be used to (i) build a neural network-based generator model that interacts with a power grid simulator or (ii) shadow the true generator's transient response. First, we develop a data-driven Deep Operator Network (DeepONet) to approximate the infinite-dimensional solution operator
On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
The RemOve-And-Retrain (ROAR) benchmark is widely used to evaluate feature attribution methods, yet its validity remains underexplored from an information-theoretic perspective. We show that model- and data-agnostic post-processing of attribution maps (transformations that, by the data processing inequality, \emph{cannot} add information about the decision function) can often improve ROAR scores. This means that an i
ResidualPlanner+: a scalable matrix mechanism for marginals and beyond
Noisy marginals are a common form of confidentiality protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation. Privacy mechanisms that provide unbiased noisy answers to linear queries (such as marginals) are known as matrix mechanisms. We propose ResidualPlanner and ResidualPlanner+, two highly scalable m
Context selectivity with dynamic availability enables lifelong continual learning
"You never forget how to ride a bike", -- but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capability, referred to as lifelong learning (or continual learning, CL), are unknown. We suggest a bio-plausible meta-plasticity rule building on classical wor
Estimating Deep Learning energy consumption based on model architecture and training environment
To raise awareness of the environmental impact of deep learning (DL), many studies estimate the energy use of DL systems. However, energy estimates during DL training often rely on unverified assumptions. This work addresses that gap by investigating how model architecture and training environment affect energy consumption. We train a variety of computer vision models and collect energy consumption and accuracy metri
QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS
In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (S
Plug-and-Play image restoration with Stochastic deNOising REgularization
Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Model
Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction
Document collections of various domains, e.g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both human users and structure-aware models. We propose to identify the typical structure of document within a collection, which requires to capture recurring topics across the collection, while abstracting over arbitrary header paraphrases, and gr
Geometry of Lightning Self-Attention: Identifiability and Dimension
We consider function spaces defined by self-attention networks without normalization, and theoretically analyze their geometry. Since these networks are polynomial, we rely on tools from algebraic geometry. In particular, we study the identifiability of deep attention by providing a description of the generic fibers of the parametrization for an arbitrary number of layers and, as a consequence, compute the dimension
Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments
In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We sho
P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction
Ever since the Dennard scaling broke down in the early 2000s and the frequency of the CPUs stalled, vendors have started to increase the core count in each CPU chip at the expense of introducing heterogeneity, thus ushering the era of NUMA and Chiplet processors. Since then, the heterogeneity in the design space of hardware has only increased to the point that DBMS performance may vary significantly up to an order of
Competition and Diversity in Generative AI
Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or similar AI models appears to lead to more homogeneous behavior. Our work begins with the observation that there is a force pushing in the opposite direction: competition. When producers compete with one another (e.g., for customers or attention
Review of Fruit Tree Image Segmentation
Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 158 relevant papers collected using a newly designed crawling
Is Stochastic Gradient Descent Effective? A PDE Perspective on Machine Learning processes
In this paper we analyze the behaviour of the stochastic gradient descent (SGD), a widely used method in supervised learning for optimizing neural network weights via a minimization of non-convex loss functions. Since the pioneering work of E, Li and Tai (2017), the underlying structure of such processes can be understood via parabolic PDEs of Fokker-Planck type, which are at the core of our analysis. Even if Fokker-
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor strongly convex. Beyond conventional perspectives and techniques, we view quantile SGD iteration as an irreducible, periodic, and positive recurrent Markov chain, which cyclically converges to its unique stationary distribution regardless o
WildIFEval: Instruction Following in the Wild
Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge. In this work, we introduce WildIFEval - a large-scale dataset of 7K real user instructions with diverse, multi-constraint conditions. Unlike prior datasets, our collection spans a broad lexical and topical spectrum of constraints, extracted from natural user in
Oncomorphic neural agent populations for resource-limited sequential learning
Distributed artificial intelligence (AI) often operates under sequential task exposure, uneven compute, and decentralized coordination. Here, we present a cancer-inspired, or oncomorphic, multi-agent framework in which simulated neural agents can replicate, mutate their neural network architecture, migrate across task environments, undergo ecological turnover, and recruit learning/ecological resources from a finite s
Radar-Guided Polynomial Fitting for Metric Depth Estimation
We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps. Unlike existing approaches that rely on complex architectures or expensive sensors, our method is grounded in a fundamental insight: although MDE models often infer reasonable local depth s
Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning
Multimodal agents, which integrate a controller e.g., a vision language model) with external tools, have demonstrated remarkable capabilities in tackling complex multimodal tasks. Existing approaches for training these agents, both supervised fine-tuning and reinforcement learning, depend on extensive human-annotated task-answer pairs and tool trajectories. However, for complex multimodal tasks, such annotations are
Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning
Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall. Analysis of production traces reveals a dynamic bursty-group pattern in which sets of models become active together and shift over time; existing space- and time-sharing approaches lack principled mechanisms to adapt to this variability, forci
Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks
We study function spaces parametrized by neural networks, referred to as neuromanifolds. Specifically, we focus on deep Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) with an activation function that is a sufficiently generic polynomial. First, we address the identifiability problem, showing that, for almost all functions in the neuromanifold of an MLP, there exist only finitely many paramete
Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\mathcal{G}
ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Post-hoc interpretability methods typically attribute a model's behavior to its components, data, or training trajectory in isolation, and are often tied to a particular level of granularity along the local-to-global spectrum. This leads to explanations that lack a unified view and may miss key interactions. We present ExPLAIND, a theoretically grounded, unified framework that integrates model components, data, a
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