Record 10062026 · captured 2026-08-25
The world looked up Victor Wembanyama. 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.
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
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
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
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.
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
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
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
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
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
.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
Peddi is a 2026 Indian Telugu-language sports action drama film written and directed by Buchi Babu Sana. Produced by Venkata Satish Kilaru under Vriddhi Cinemas and co-produced by Ishan Saksena under IVY Entertainment and presented by Mythri Movie Makers and S
Stephon Javonte Castle is an American professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He played college basketball for the UConn Huskies, winning a national championship in 2024. He was a consensus five-st
Masters of the Universe (2026 film)
Masters of the Universe is a 2026 American sword-and-sorcery film based on the media franchise by Mattel. It is the second live-action film adaptation, the 1987 film was the first. It was directed by Travis Knight and written by Chris Butler, Aaron Nee, Adam N
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
Nithya V. Raman is an American urban planner, activist, and politician serving as the Los Angeles city councilmember for the 4th district since 2020. Raman, a member of the Democratic Party and the Democratic Socialists of America, defeated incumbent councilme
Rachel Jane Nickell was a British woman who was stabbed to death on Wimbledon Common in southwest London on 15 July 1992. The initial police investigation of the crime resulted in the arrest in controversial circumstances of an innocent man, who was acquitted.
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
Gephyromantis tschenki, commonly known as Tschenk's Madagascar frog, is a species of frogs in the family Mantellidae. It is endemic to Madagascar. Its natural habitats are subtropical or tropical moist lowland forests, subtropical or tropical moist montane for
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 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
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Timothée Hal Chalamet is an American and French actor. Known for his work in a diverse range of blockbusters and independent films, he is the recipient of numerous accolades including an Actor Award, a Golden Globe Award, and two Critics' Choice Awards, in add
Spencer William Pratt is an American reality television personality. In 2007, he began dating Heidi Montag, a primary cast member of the reality television series The Hills and came to prominence after being cast in the series. A feud between them and Montag's
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
Scary Movie is a 2026 American parody film directed by Michael Tiddes and written by Marlon Wayans, Shawn Wayans, Keenen Ivory Wayans, Craig Wayans, and Rick Alvarez. It is the sixth installment in the Scary Movie film series and has been referred to as the sp
Teach You a Lesson (Korean: 참교육) is a 2026 South Korean action school drama television series written by Lee Nam-kyu, Kim Da-hee, and Moon Jong-ho, directed by Hong Jong-chan, and starring Kim Mu-yeol, Lee Sung-min, Jin Ki-joo, and Pyo Ji-hoon. Based on the Na
Ethan Samuel Slater is an American actor and singer. He played the character of SpongeBob SquarePants in the 2016 musical of the same name, for which he won a Drama Desk Award and received a nomination for the Tony Award for Best Actor in a Musical. He played
Robert Clive Napper is an English serial killer and rapist. He has been convicted of two murders, one manslaughter, two rapes and two attempted rapes. He was sentenced to indefinite detention at Broadmoor Hospital on 18 December 2008 for the manslaughter of Ra
XXX may refer to:
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Open Korean Corpora: A Practical Report
Korean is often referred to as a low-resource language in the research community. While this claim is partially true, it is also because the availability of resources is inadequately advertised and curated. This work curates and reviews a list of Korean corpora, first describing institution-level resource development, then further iterate through a list of current open datasets for different types of tasks. We then p
Time series forecasting from partial observations via Non-negative Matrix Factorization
In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values. In this paper, we introduce the Sliding Mask Method (SMM) for forecasting multiple nonnegative time series by means of nonnegative matrix completion: observed noisy values and forecast/missing values are collected into matrix form, and learning is achieved by representing its rows as a convex combinati
Belief Acquisition as Stochastic Filtering
This paper studies how belief acquisition can be accomplished using stochastic filtering. First, a theoretical foundation for empirical beliefs is outlined. Then stochastic filtering in this context is studied. The paper introduces factored conditional filters, new filtering algorithms for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of the algorith
Scalably learning quantum many-body Hamiltonians from dynamical data
The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques
When beginners learn to speak a non-native language, it is difficult for them to judge for themselves whether they are speaking well. Therefore, computer-assisted pronunciation training systems are used to detect learner mispronunciations. These systems typically compare the user's speech with that of a specific native speaker as a model in units of rhythm, phonemes, or words and calculate the differences. Howeve
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Gl
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning
How does scaling the number of parameters in large language models (LLMs) affect their core capabilities? We study two natural scaling techniques -- weight pruning and simply training a smaller or larger model, which we refer to as dense scaling -- and their effects on two core capabilities of LLMs: (a) recalling facts presented during pre-training and (b) processing information presented in-context during inference.
The Emergence of Reproducibility and Generalizability in Diffusion Models
In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs. We confirm this phenomenon through comprehensive experiments, implying that different diffusion models consistently reach the same data dis
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant performance in well-known environments, it remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications. To improve usability, DRL must demonstrate trustworthiness and robustness. A w
Mixtures of Neural Operators Reduce Active Complexity in Operator Learning
Operator-learning systems are not governed solely by total parameter count; for one query, the relevant bottleneck can be the model that must be loaded and evaluated. We study this distinction for classical neural operators on compact Sobolev subsets through a constructive comparison between routed mixtures of neural operators (MoNOs) and a fixed single-neural-operator construction. The comparison concerns expert-act
Standard Language Ideology in AI-Generated Language
Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted more prestige, authority, and legitimacy than others. This paper contributes a sociotechnically grounded faceted taxonomy that illustrates how generative AI systems reproduce standard language ideology and its societal implications. We introduce the concept of standard AI-g
EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain
It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event extraction in scientific domain is still insufficiently supported by comprehensive datasets and tailored methods. Compared with other domains, scientific domain has two characteristics: (1) denser nuggets and events, and (2) more complex inf
3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods
3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-of-the-art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is
BadRobot: Jailbreaking Embodied LLM Agents in the Physical World
Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BadRobot, a novel attack paradi
Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals; however, concerns ari
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart from the model training, existing methods requires the collaboration of all clients in the model inference. However, the model inference is probably maintained for service in a long time, while the collaboration, especially when the clients
The role of data embedding in quantum autoencoders for improved anomaly detection
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects of three data embedding techniques, data re-uploading, parallel embedding, and alternate embedding, on the representability and effectiveness of QAEs in detecting anomalies. Our findings reveal that even with relatively simple variational
Detecting LGBTQ+ Instances of Cyberbullying
Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ commu
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector
The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptiv
Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored. Existing diversity metrics such as Vendi and RKE, which are based on the von Neumann and Rényi entropies of kernel matrices, were developed for unconditional models and cannot distinguish prompt-induced from model-induced variability. We address this gap by intro
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexity of these models. State-of-the-art saliency methods generate local explanations highlighting the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction. On the other h
Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music
We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representations and discrete tokens simultaneously as part of a single architecture. There has been a huge surge in generative audio, speech, and music models that utilize discrete audio tokens derived from neural compression algorithms, e.g. ENCODEC. However, one of the major drawbacks of
CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-deve
Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines
The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However, these systems are vulnerable to adversarial attacks, especially ranking manipulation attacks, where attackers craft webpage content to manipulate the LLM's ranking and promote specific content, gaining an unfair advantage over competitors. In this paper, we study the dynam
Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet)
Notable events recorded on this day and month across all years.