Record 12082026 · captured 2026-08-25
The world looked up Spider-Man: Brand New Day. 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.
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
Lucy Clare Davis is an English actress and comedian known for playing Dawn Tinsley in the BBC mockumentary television sitcom The Office (2001–2003), Hilda Spellman in the Netflix supernatural horror television series Chilling Adventures of Sabrina (2018–2020),
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
The Last House is a 2026 American science fiction horror film written by Matthew Robinson, and directed by Louis Leterrier. It stars Greta Lee and Wagner Moura. The film follows a family that finds themselves inexplicably sealed in their home, with the whole w
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
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
Abdulrahman Mohamed El-Sayed, commonly known as Abdul El-Sayed, is an American politician and epidemiologist who is the Democratic nominee in the 2026 United States Senate election in Michigan. A progressive member of the Democratic Party, El-Sayed was a candi
.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
Anne Jacqueline Hathaway is an American actress. Her accolades include an Academy Award, a British Academy Film Award, a Golden Globe Award, and a Primetime Emmy Award. Her films have grossed over $6.8 billion worldwide.
DC is a 2026 Indian Tamil-language romantic action film directed by Arun Matheswaran and produced by Kalanithi Maran's Sun Pictures. The film stars Lokesh Kanagaraj, Wamiqa Gabbi and Sanjana Krishnamoorthy. It follows Das, an outlaw and Chandra, a brutalised s
Robert Norman Davis, known by his stage name Jasper Carrott, is an English comedian, writer, actor, singer and television presenter. His credits include An Audience with Jasper Carrott (1978), The Secret Policeman's Other Ball (1982), Carrott's Lib (1982–1983)
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in Martin's A Song of Ice and Fire franchise. Based on parts
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
Verity is a 2018 psychological romantic thriller novel by American author Colleen Hoover. The narrative follows Lowen Ashleigh, a writer who is hired to complete a bestselling book series after its original author, Verity Crawford, is left unable to continue d
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
Solar eclipse of August 12, 2026
A total solar eclipse occurred at the Moon's descending node of orbit on Wednesday, 12 August 2026, with a magnitude of 1.0386. Totality occurred in a narrow path across Earth's surface, with the partial solar eclipse visible over a surrounding region thousand
The third season of the American fantasy drama television series House of the Dragon premiered on HBO on June 21, 2026, in the United States and concluded on August 9, 2026. It consists of eight episodes, each of approximately one hour. The season covers the e
Francesca Hong is an American politician, former chef, and business owner who has represented Wisconsin's 76th Assembly district in the Wisconsin State Assembly since 2021. She is a member of the Democratic Party and the Democratic Socialists of America (DSA).
Watson Brake is an archaeological site in present-day Ouachita Parish, Louisiana, from the Archaic period. Dated to about 5400 years ago, Watson Brake is considered the oldest earthwork mound complex in North America. It is older than the Ancient Egyptian pyra
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
DiJonai Victoria Carrington is an American professional basketball player for the Chicago Sky of the Women's National Basketball Association (WNBA). She played college basketball for Stanford and Baylor before being drafted 20th overall by the Connecticut Sun
The Boeing C-32 is the United States Air Force designation for variants of the Boeing 757 in military service. Two variants exist, filling different parts of the military passenger transport role. The C-32A serves the Special Air Mission, providing executive t
Owain Sebastian Yeoman is a Welsh actor, best known for playing CBI Agent Wayne Rigsby in the CBS series The Mentalist. His additional credits include The Nine, Kitchen Confidential, Turn, Generation Kill, and Emergence.
Bashar Hafez al-Assad is a former Syrian politician, medical doctor, and military officer who served as the president of Syria from 2000 until his overthrow in 2024 after the Syrian civil war. As president, Assad was commander-in-chief of the Syrian Arab Armed
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
List of Marvel Cinematic Universe films
The Marvel Cinematic Universe (MCU) centers on American superhero films produced by Marvel Studios, based on characters that appear in publications by Marvel Comics. The MCU is the shared universe in which all of the films are set. Marvel Studios has released
On 10 August 2026, a Mw 7.4–7.5 earthquake struck the department of Chocó in Colombia, killing at least 331 people, injuring more than 4,600 others, leaving 240 confirmed missing and causing extensive damage across the country. The earthquake was felt across m
2026 in film is an overview of events in the film industry scheduled to occur in 2026. Best Picture Academy Award-winners All Quiet on the Western Front and Cimarron entered the public domain this year.
Sterling Point is an American drama television series created by Megan Park and starring Ella Rubin, Jacob Whiteduck-Lavoie, Amélie Hoeferle, Daniel Quinn-Toye, Bo Bragason, and Keen Ruffalo. The series premiered on Amazon Prime Video on August 5, 2026. In Aug
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DNNs are capable of generalizing to objects in novel orientations by disseminating orientation-invariance obtained from familiar objects seen from many viewpoints. This capability strengthens when training the DNN with an increasing number of
Representation and Invariance in Reinforcement Learning
Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework. In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks. We introduce a criterion which is sufficient for relative intellige
Estimation-of-distribution algorithms (EDAs) are optimization algorithms that learn a distribution on the search space from which good solutions can be sampled easily. A key parameter of most EDAs is the sample size (population size). If the population size is too small, the update of the probabilistic model builds on few samples, leading to the undesired effect of genetic drift. Too large population sizes avoid gene
Graphical Models of False Information and Fact Checking Ecosystems
The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised and globalised society. A lot of research has been done to better understand different aspects of false information online such as behaviours of different actors and patterns of spreading, and also on better detection and prevention of such information using technical and socio
Robust, randomized preconditioning for kernel ridge regression
We investigate preconditioned conjugate gradient methods for kernel ridge regression (KRR) problems with a moderate to large number of data points ($10^4 \leq N \leq 10^7$). We develop and analyze two randomized preconditioners with complementary guarantees. For full-data KRR, RPCholesky preconditioning requires $O(N^2)$ arithmetic operations to achieve fixed accuracy under sufficiently rapid eigenvalue decay of the
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator. Because WRLS only provides point estimates, previous methods typically construct surrogate distributions when aiming to perform Bayesian-like randomized exploration. To more properly establish the Bayesian principles, we introduce Weighted Sequential Bayesian (WSB) infe
Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically provable bounds to protect such data, previous work has focused almost exclusively on unweighted ERM. We consider weighted ERM (wERM) -- an important generalization where individual contributions to the objective function vary. We propose the
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients. Several standard analyses assume independent stochastic-gradient samples, whereas a common finite-sum implementation reshuffles the data and processes them sequentially. We study this variant, signSGD with random reshuffling (SignRR), and show that reshuffling does not in general repair the bias c
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable and robust performance in various applications. Current optimizers often struggle with zero-shot optimization and require intricate hyperparameter tuning to adapt to new tasks. To address this, we propo
ZeroPur: Succinct Training-Free Adversarial Purification
Adversarial purification is a kind of defense technique that can defend against various unseen adversarial attacks without modifying the victim classifier. Existing methods often depend on external generative models or cooperation between auxiliary functions and victim classifiers. However, retraining generative models, auxiliary functions, or victim classifiers relies on the domain of the fine-tuned dataset and is c
We propose a scalable variational Bayes method for statistical inference for a single or pre-specified low-dimensional subset of the coordinates of a high-dimensional parameter in sparse linear regression. Our approach relies on assigning a mean-field approximation to the nuisance coordinates and carefully modelling the conditional distribution of the target given the nuisance. This requires only a preprocessing step
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in va
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is crucial for optimizing computational resources to accurately estimate expected rewards for the agent. In scenarios with sparse rewards, such as robotic control systems, achieving this balance is particularly challenging. However, given that
ADIOS: Antibody Development via Opponent Shaping
Anti-viral therapies are typically designed to target only the current strains of a virus, a myopic response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both defend against and actively influence viral evolution. This motivates our met
FedSlate:A Federated Deep Reinforcement Learning Recommender System
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems do not fully exploit the relevance of individual user behavior across different platforms. One potential solution is to aggregate data from various platforms in a centralized location and use the aggregated data for training. However, this
Predicting total time to compress a video corpus using online inference systems
Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compression time, and thus estimating the cost of video compression.
Traffic simulations are commonly used to optimize urban traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control, particularly in intelligent transportation systems involving connected automated vehicles. Multi-agent reinforcement learning (MARL) is particularly effective for learning control strategies for traffic lights in a network using iterative simulations.
Regression and Classification with Single-Qubit Quantum Neural Networks
The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other. Motivated by the rich connection between these areas, we use a resource-efficient and scalable Single-Qubit Quantum Neural Network (SQQNN) for both regression and classification tasks using a new data uploading technique. The SQQNN leverage
KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning
This paper proposes a novel learning approach for designing Kazantzis-Kravaris or nonlinear Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer's state is then mapped back to the original system coordinates via the inve
Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to revea
Quantum-aware Transformer model for state classification
Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural ne
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding
Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-Judge framework, which uses prompted LLMs to evaluate response quality, is appealing due to its scalability, low cost, and strong correlations with human stylistic preferences. However, it remains unclear how accurately these methods can as
GranQ: Efficient Channel-wise Quantization via Vectorized Pre-Scaling for Zero-Shot QAT
Zero-shot quantization (ZSQ) enables neural network compression without original training data, making it a promising solution for restricted data access scenarios. To compensate for the lack of data, recent ZSQ methods typically rely on synthetic inputs generated from the full-precision model. However, these synthetic inputs often lead to activation distortion, especially under low-bit settings. To mitigate this, ex
Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking
Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized imitation of high-value creative works. Existing copyright protection techniques mainly focus on visual media, leaving the protection of creative writing largely unexplored. In this work, we investigate a new challenge: verifying whether AI-
Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting
Accurate LiDAR-camera calibration is crucial for multi-sensor systems. However, traditional methods often rely on physical targets, which are impractical for real-world deployment. Moreover, even carefully calibrated extrinsics can degrade over time due to sensor drift or external disturbances, necessitating periodic recalibration. To address these challenges, we present a Targetless LiDAR-Camera Calibration (TLC-Cal
Notable events recorded on this day and month across all years.