Record 20082026 · captured 2026-08-25
The world looked up Hayden Panettiere. 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.
Hayden Lesley Panettiere was an American actress and singer. She starred as Claire Bennet on the NBC superhero series Heroes (2006–2010), Kirby Reed in the slasher horror franchise Scream (2011–2023), Juliette Barnes in the ABC/CMT musical drama series Nashvil
Natalie Harp is an American political aide and former television anchor who has served as special assistant and executive assistant to the President of the United States since 2025.
Google LLC is an American multinational technology corporation focused on information technology, online advertising, search engine technology, email, cloud computing, software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Frank Lee Beard was an American musician who was the longtime drummer of the rock band ZZ Top. He and bassist Dusty Hill performed with American Blues before the two joined guitarist Billy Gibbons on ZZ Top. The trio enjoyed early success with the hits "Tush"
Awarapan 2 is a 2026 Indian Hindi-language action thriller film directed by Nitin Kakkar, written by Kakkar, Bilal Siddiqui and Vishesh Bhatt, and produced under his banner Vishesh Films. A sequel to the 2007 film Awarapan, the film stars Emraan Hashmi, Disha
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 Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Wladimir Klitschko is a Ukrainian former professional boxer who competed from 1996 to 2017. He held multiple heavyweight world championships between 2000 and 2015, including unified titles between 2008 and 2015. During this time he also held the International
ZZ Top is an American rock band formed in Houston, Texas, in 1969. For five decades, the band had consisted of vocalist-guitarist Billy Gibbons, drummer Frank Beard and bassist-vocalist Dusty Hill, until Hill's death in 2021 and Beard's death in 2026. ZZ Top d
Angela Nixon is an American politician who has served as a member of the Florida House of Representatives since 2020. A member of the Democratic Party and the Democratic Socialists of America, she is the Democratic nominee in the 2026 United States Senate spec
Byron Lowell Donalds is an American politician serving as the U.S. representative for Florida's 19th congressional district since 2021. His congressional district includes much of Southwest Florida. Previously, he served in the Florida House of Representatives
Jansen Rane Panettiere was an American actor, known for his roles in films The Secrets of Jonathan Sperry, The Perfect Game, The Martial Arts Kid, and How High 2. He had also provided the voice roles of Periwinkle in the sixth and final season of the Nick Jr.
Jason Atta Kwei Arday was a British academic who was a professor of sociology of education at the University of Cambridge from 2023 to 2026. Arday received international attention and resigned amid accusations of plagiarism, false claims in his research, and f
David Wilson Jolly is an American attorney, politician, and lobbyist who served as the U.S. representative for Florida's 13th congressional district from 2014 to 2017 as a Republican. After leaving office, Jolly became a prominent Republican critic of Presiden
Lanterns is an American superhero television series created by Chris Mundy, Damon Lindelof, and Tom King for HBO, based on the DC Comics Green Lantern characters Hal Jordan and John Stewart. It is the third television series in the DC Universe (DCU). It featur
2026 Men's FIH Hockey World Cup
The 2026 Men's FIH Hockey World Cup is the 16th edition of the Men's FIH Hockey World Cup, the quadrennial world championship for men's national field hockey teams organized by the International Hockey Federation. It is being held from 15 to 30 August 2026 in
Alexander Semyon Vindman is a retired United States Army lieutenant colonel who served as the Director of European Affairs for the National Security Council (NSC) until 2020. He came to national attention in 2019 as a witness in Congress's impeachment inquiry
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
William Frederick Gibbons is an American rock musician who is the guitarist, primary songwriter, lead vocalist and only constant member of ZZ Top. He began his career in Moving Sidewalks, who recorded Flash (1969) and opened four dates for the Jimi Hendrix Exp
The End of Oak Street is a 2026 American science fiction survival film written, co-produced, and directed by David Robert Mitchell. It stars Anne Hathaway, Ewan McGregor, Maisy Stella and Christian Convery as a family whose suburban neighborhood has been trans
Ezri Ngoyo Konsa is an English professional footballer who plays as a centre-back or right-back for Premier League club Arsenal and the England national team.
Manav Jagdusakumar Suthar is an Indian cricketer who plays for the Indian national team. He is a bowling all-rounder, who bats left-handed and bowls slow left-arm orthodox spin. He represents Rajasthan in domestic cricket and Gujarat Titans in the Indian Premi
List of highest-grossing films
Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising. However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a
Thomas Jonathan Ossoff is an American politician who has served as the senior United States senator from Georgia since 2021. He is the youngest incumbent U.S. senator, and a member of the Democratic Party. Ossoff is also the youngest senior senator of the Unit
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Joe Michael "Dusty" Hill was an American musician who was the bassist of the rock band ZZ Top for more than 50 years. He also sang backing and lead vocals and played keyboards.
Vishwanath & Sons is a 2026 Indian Tamil language romance and family drama film written and directed by Venky Atluri. Produced by Sithara Entertainments and Fortune Four Cinemas, the film stars Suriya, alongside Mamitha Baiju, Raadhika Sarathkumar, and Raveena
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
Rodrigo Hernández Cascante, known simply as Rodri or Rodrigo, is a Spanish professional footballer who plays as a defensive midfielder for La Liga club Barcelona and captains the Spain national team. Widely regarded as one of the best midfielders in the world,
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Machine Learning Systems: A Survey from a Data-Oriented Perspective
Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including parallelization, in light of the success of machine learning in natural language processing. However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack
Corrections of Zipf's and Heaps' Laws Derived from Hapax Rate Models
The article introduces corrections to Zipf's and Heaps' laws based on systematic models of the proportion of hapaxes, i.e., words that occur once. The derivation rests on two assumptions. The first one is the standard urn model which predicts that marginal frequency distributions for shorter texts look as if word tokens were sampled blindly from a given longer text. The second assumption posits that the hapax
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation
Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability. We propose CLAP, a novel Conditional LAtent Point-diffusion model that combines geometric deep learning with denoising diffusion models to enhance 3D representations of the large in
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ev
The Systems Engineering Approach in Times of Large Language Models
Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilit
OrcaLoca: An LLM Agent Framework for Software Issue Localization
Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challenge. Current approaches often yield suboptimal results due to a lack of effective integration betwee
`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs
As large language models (LLMs) are increasingly integrated into audio-based applications, growing concerns have emerged regarding their vulnerability to audio-based adversarial attacks. These systems typically follow two architectural paradigms: cascaded pipelines, where automatic speech recognition converts audio inputs into text before LLM processing, and end-to-end large audio-language models (LALMs), which direc
Interactive Stroke-based Neural SDF Sculpting
Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these representations presents challenges due to the complex relationship between model weights and surface geometry, as well as the slow optimization required to update neural fields. Among various editing tools, sculpting stands out as a valuable operation for the graphics and model
How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors
Causal discovery methods based on the PC algorithm are proven to be sound if all structural assumptions are fulfilled and all conditional independence tests are correct. This idealized setting is rarely given in real data. In this work, we first analyze how local errors can propagate throughout the output graph of a PC-based method, highlighting how consequential seemingly innocuous errors can become. Next, we introd
Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling
Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a non-trivial feat that requires fine-tuning and can lead to so-called hallucinations, that is, the generation of images that are unrealistic. In this work, we explore image generation using flow matchi
Using Machine Learning to Detect Fraudulent SMSs in Chichewa
SMS enabled fraud is of great concern globally. Building classifiers based on machine learning for SMS fraud requires the use of suitable datasets for model training and validation. Most research has centred on the use of datasets of SMSs in English. This paper introduces a first dataset for SMS fraud detection in Chichewa, a major language in Africa, and reports on experiments with machine learning algorithms for cl
There is a prevalent debate in machine learning about whether practitioners should train models to optimize a task-agnostic objective (e.g., cross entropy) or incorporate the downstream decision task into the optimization objective (e.g., weighted cross entropy). In ideal settings, like those with infinite data and infinite model capacity, the two approaches are statistically equivalent for the downstream decision ta
Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum
With the rapid development of image synthesis techniques, AI-generated images have become increasingly realistic, which heightens the potential risk associated with their misuse and creates a growing need for reliable detection. However, the growing diversity of generative models makes it increasingly difficult for detectors to generalize to images produced by unseen generators. Most existing methods rely on artifact
Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. While Graph Convolutional Networks (GCNs) are common, they are limited by their reliance on fixed skeletal graphs. To overcome this, we propose the Sequential Spatio-Temporal Attention Network (SSTAN), a novel Transformer-based architecture. Our model employs a hierarchical, stacked desig
Investigating Vision-Language Model for Point Cloud-based Vehicle Classification
Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability compared to passenger vehicles. A deeper understanding of truck characteristics is essential for enhancing the safety perspective of cooperative autonomous driving. Traditional LiDAR-based truck classification methods rely on extensive manual annotations, which makes them labor-intensive and costly. The rapid adva
Diffusion models excel at generating high-quality images but can memorize and reproduce harmful concepts when prompted. Although fine-tuning methods have been proposed to unlearn a target concept, they struggle to fully erase it while maintaining generation quality on other concepts, leaving models vulnerable to jailbreak attacks. Existing jailbreak methods demonstrate this vulnerability but offer limited insight int
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition
Online anomaly detection is essential in fields such as cybersecurity, healthcare, industrial monitoring, and telecommunications, where promptly identifying deviations from expected behavior can avert critical failures or security breaches. While numerous anomaly scoring methods based on supervised or unsupervised learning have been proposed, the only existing approach capable of providing assumption-free guarantees
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia com
A Reality Check of Language Models as Formalizers on Constraint Satisfaction Problems
Recent work shows superior performance when using large language models (LLMs) as formalizers instead of as end-to-end solvers for symbolic reasoning problems. Given the problem description, the LLM generates a formal program that derives a solution via an external solver. We systematically investigate the formalization capability of LLMs on real-life constraint satisfaction problems on 4 benchmarks, 6 LLMs, and 2 ty
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities, but whether they navigate this same compression-meaning trade-off remains unclear. We apply an Information Bottleneck framework to compare human conceptual structure with embeddings from 40+ LLMs using classic categorization benchmarks. We
CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based representations, especially beyond simple color changes, remains challenging. In this work, we introduce CLIPGaussian, the first unified style transfer framework that supports text- and image-gui
Weak-to-Strong Generalization via Bregman Bias-Variance Decomposition
Weak-to-strong generalization (W2SG) is the phenomenon in which a powerful student model, trained on labels produced by a weaker teacher, ultimately outperforms the teacher on the target task. In this work, we theoretically investigate how W2SG can arise via a generalized bias-variance decomposition under Bregman divergence. We show that the expected population risk gap between the student and the teacher is characte
DeGLIF for Label Noise Robust Node Classification using GNNs
Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out Influence Function. DeGLIF uses a small set of clean data and the leave-one-out influence function to make label noise robust node-level prediction on graph data. Leave-one-out influence function app
MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these factual errors at test-time, particularly without ground-truth labels, is essential for building trustworthy autonomous agents. We propose MMD-Flagger, an hallucination detection method that utilizes Maximum Mean Discrepancy (MMD) and moni
Notable events recorded on this day and month across all years.