Record 29062026 · 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
Stephen Antunes Eustáquio is a Canadian professional soccer player who plays as a midfielder for EFL Championship side Swansea City and vice-captains the Canada national team.
2026 FIFA World Cup knockout stage
The knockout stage of the 2026 FIFA World Cup was the second and final stage of the competition, following the group stage. Played from June 28 to July 19, 2026, the knockout stage ended with the final, held at MetLife Stadium in East Rutherford, New Jersey. T
.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
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
Supergirl is a 2026 American superhero film based on the DC Comics superheroine Kara Zor-El / Supergirl. Directed by Craig Gillespie and written by Ana Nogueira, it is the second film in the DC Universe (DCU). Milly Alcock stars in the title role, alongside Ma
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
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
Citizen Vigilante is a 2026 action-thriller film produced, written, and directed by Uwe Boll. It stars Armie Hammer as Michael Sanders, a vigilante enraged by the breakdown of law and order who targets criminals and rapists, most of whom are migrants, and the
Krishnaswamy Bhagyaraj was an Indian filmmaker, actor, musician and politician. He worked predominantly in Tamil cinema, while also directing and acting in films in other Indian languages. He wrote and directed more than 25 films and acted in over 75 films, an
Lucas Federico Trejo is an Argentine football player who plays as a defender for Venezuelan Segunda División club Marítimo.
The Sheep Detectives is a 2026 mystery comedy-drama film directed by Kyle Balda and written by Craig Mazin, based on the 2005 novel Three Bags Full by Leonie Swann. The film features an ensemble cast including Hugh Jackman, Nicholas Braun, Nicholas Galitzine,
Jesse Alan Marsch is an American professional soccer coach and former player who is the head coach of the Canada men's national team. Marsch played 14 seasons as a midfielder in Major League Soccer (MLS) with D.C. United, Chicago Fire, and Chivas USA, winning
Blast is a 2026 Indian Tamil-language action thriller film directed by Subash K. Raj in his debut and produced by AGS Entertainment. The film stars Arjun Sarja, Abhirami and Preity Mukhundhan, with John Kokken, Vivek Prasanna, Arjun Chidambaram, Dileepan and P
I Will Find You is an American crime drama miniseries made for Netflix, adapted from the 2023 novel of the same name by Harlan Coben, who served as executive producer. The miniseries stars Sam Worthington, Britt Lower, Milo Ventimiglia, and Erin Richards. It p
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 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
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
James David Rodríguez Rubio is a Colombian footballer who plays as an attacking midfielder. As of July 2026, he is a free agent, and his last team was Minnesota United FC of Major League Soccer (MLS). He is a international for the Colombia national football te
Alphonso Boyle Davies is a professional soccer player who plays as a left-back, winger, or wing-back for Bundesliga club Bayern Munich and captains the Canada national team. Regarded as one of the best left-backs in the world, Davies has earned the nickname "t
Tubifex tubifex, also called the sludge worm, sewage worm, or simply tubifex worm, is a species of tubificid segmented worm which inhabits the sediments of lakes and rivers on several continents. Tubifex tubifex is a slender segmented annelid that can reach le
List of FIFA World Cup top goalscorers
Players have scored more than 3,000 goals in the 23 men's FIFA World Cup tournaments, the goal record includes own goals scored, but not counting penalty shoot-outs. Since the first goal, by French player Lucien Laurent in 1930, nearly 1,300 footballers have s
Melvin James Brooks is an American actor, comedian, film director, songwriter and playwright. With a career spanning nine decades, he is known for a variety of successful farces and parodies. A recipient of numerous accolades, he is one of the few people to wi
Cape Verde, also referred to in English by its Portuguese name Cabo Verde, and known officially as the Republic of Cabo Verde, is an archipelagic country in the eastern Atlantic Ocean, off the coast of West Africa. It consists of ten volcanic islands with a co
The 2026 Night of Champions, also promoted as Night of Champions: Riyadh, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. It is the 12th Night of Champions and took place on Saturday, June 27, 2026,
Benjamin Andrew Stokes is an English former international cricketer who captained the England Test team from 2022 to 2026. Stokes played for England in all three formats and is regarded as one of England's greatest all-rounders. In domestic cricket, he represe
Sophie Elizabeth Cunningham is an American professional basketball player for the Indiana Fever of the Women's National Basketball Association (WNBA). She played college basketball for the Missouri Tigers.
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
Elegua is an Orisha, a deity of roads in the Yoruba religion, Santería, Winti, Umbanda, Quimbanda, and Candomblé.
Toy Story 5 is a 2026 American animated comedy-drama film produced by Pixar Animation Studios for Walt Disney Pictures. Directed by Andrew Stanton and written by Stanton and Kenna Harris, it is the fifth main installment in the Toy Story film series and the se
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space
Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially deep residual networks (ResNets), tend to learn during training remain insufficiently characterized. To this end, we model the forward propagation of deep residual networks using continuity equations, in which the measure is conserved and infinite curves in
There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on the performance metrics used to evaluate and compare them -- resulting in a growing body of literature to evaluate and compare metrics. This paper strives for a more balanced perspective on classifier performance metrics by
Multiperiodic Processes: Ergodic Sources with a Sublinear Entropy
Several explicit stochastic processes are known to satisfy Hilberg's law, a power-law growth of block entropy conjectured for natural language and recently connected to the neural scaling law. Existing examples either possess a positive Shannon entropy rate, are non-ergodic, or require comparatively involved constructions. We introduce multiperiodic processes, a new class of stationary ergodic processes over the
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive data
Arrows of Time for Large Language Models
We study the probabilistic modeling performed by Autoregressive Large Language Models (LLMs) through the angle of time directionality, addressing a question first raised in (Shannon, 1951). For large enough models, we empirically find a time asymmetry in their ability to learn natural language: a difference in the average log-perplexity when trying to predict the next token versus when trying to predict the previous
Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding
Kernel herding belongs to a family of deterministic quadratures that seek to minimize the maximum mean discrepancy (MMD), that is, the worst-case integration error over a reproducing kernel Hilbert space (RKHS). These MMD minimization procedures come with strong experimental support, but comparatively less theoretical footing. In particular, apart from recent progress in distribution compression, little has been prov
ROME: Memorization Insights from Text, Logits and Representation
Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is highly time-consuming. To address this challenge, this paper proposes an innovative approach named ROME that bypasses direct processing of the training data. Specifically
In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can address this, but existing models typically require substantial amounts of data to train effectively, contradicting our objective to solve data scarcity. To address this challe
Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is scarce. In this paper, we propose EMDiffusion, an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean image
Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment
User-generated videos (UGVs) uploaded from mobile phones to social media sites like YouTube and TikTok are short and non-repetitive. We summarize a transitory UGV into several keyframes in linear-time via fast graph sampling based on Gershgorin disc alignment (GDA). Specifically, we first model a sequence of $N$ frames in a UGV as an $M$-hop path graph $\cG^o$ for $M \ll N$, where the similarity between two frames wi
ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering
Domain-specific question answering remains challenging for language models, given the deep technical knowledge required to answer questions correctly. This difficulty is amplified for smaller language models that cannot encode as much information in their parameters as larger models. The "Specializing Large Language Models for Telecom Networks" challenge aimed to enhance the performance of two small language
iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework
Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class. Cost-sensitive learning (CSL) addresses this issue by assigning higher penalties to minority-class misclassifications. However, conventional CSL typically applies a uniform penalty to all minority-class instances, ignoring the fact that minority samples may diff
MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis
Recent works in volume rendering, \textit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with the help of the learned implicit neural radiance field or 3D Gaussians. Rendering on top of an explicit representation, the vanilla 3DGS and its variants deliver real-time efficiency by optimizing the parametric model with single-view supervision per iteration during t
Continual Memorization of Factoids in Language Models
As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is fine-tuning them directly on new knowledge. However, recent studies have shown that fine-tuning for memorization may be ineffective in storing knowledge or may exacerbate hallucinations. In this work, we introduce a setting we call continual memorization, where a model mu
Tortho-Gaussian: Splatting True Digital Orthophoto Maps
True Digital Orthophoto Maps (TDOMs) are essential products for digital twins and Geographic Information Systems (GIS). Traditionally, TDOM generation involves a complex set of traditional photogrammetric process, which may deteriorate due to various challenges, including inaccurate Digital Surface Model (DSM), degenerated occlusion detections, and visual artifacts in weak texture regions and reflective surfaces, etc
SpecFuse: Ensembling Large Language Models via Next-Segment Prediction
Ensembles of generative large language models (LLMs) are a promising way to compensate for individual model limitations, integrating the strengths of different LLMs. Existing LLM ensemble methods, however, face limitations such as first-token delay and challenges in long-range semantic collaboration between models, Moreover, they typically assume equal voting weights for all models during ensemble, ignoring task-spec
Towards Reliable Recommender Systems for Rating Data
Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place increasing pressure on the reliability of these systems. A common statistical approach behind recommender systems is so-called matrix completion, which predicts how users would rate items the
Automating RT Planning at Scale: High Quality Data For AI Training
Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is desig
Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?
The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization - the problem of identifying the geo-coordinates of a place based on visual data only. In robotics, such capabilities are particularly relevant to the global re-localization stage of the kidnapped robot problem, where a robot must recover its pose without prior knowledge of its location
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation. A common strategy is to learn features that maximize a measure of statistical dissimilarity between the class-conditional probability distributions. Information geometry, which is rooted in Riemannian geometry, provides an alternative framework for measuring class dissimilarity. It treats prob
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-context performance of arbitrary short-context LLMs by dynamically adapting their parameters to each long input. Instead of endlessly extending context windows to fit longer inputs in context, LIFT stores and absorbs the input in parameters. By
Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians
Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts. The Gumbel-Sinkhorn method, while effective, requires N*N parameters to determine a full permutation matrix, making it computationally expensive for large datasets. Low-rank matrix factorization approximations reduce memory requirements to 2NM (with
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method
Trajectory prediction, as a critical component of autonomous driving systems, has attracted the attention of many researchers. Existing prediction algorithms focus on extracting more detailed scene features or selecting more reasonable trajectory destinations. However, in the face of dynamic and evolving future movements of the target vehicle, these algorithms cannot provide a fine-grained and continuous description
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques. ML techniques can be used to detect DoH tunnels; however, their effectiveness relies on large datas
Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance
We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multi-dimensional setting, and make new arguments about the proper way to approach this generalization. Our proposed formulation maximizes the difference over orthogonal dominating rectangular ranges (d-sided rectangles in R^d), and is an integral probability metric. We also prove that the distance between a distribution and
Notable events recorded on this day and month across all years.