Record 12102026 · captured 2026-10-13
The world looked up Money in the Bank (2026). 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 Money in the Bank, also promoted as Money in the Bank: New Orleans, was a professional wrestling premium live event (PLE) produced by WWE. It was held for wrestlers from the promotion's main roster brands, Raw and SmackDown. It was the 17th annual Mon
Mathematics is a field of knowledge concerned with abstract concepts such as numbers, geometric shapes, sets, functions, and probabilities. It uses logical reasoning and proof to study and establish their properties, often expressed as theorems, formulas, and
This is a list of equations, indicating each Wikipedia page under appropriate bands of their field.
Carrie is an American supernatural horror television miniseries developed by Mike Flanagan, and produced by Amazon MGM Studios for Amazon Prime Video. The first series adaptation of the eponymous franchise, it is based on the 1974 novel by Stephen King, and is
Michael Keller Ditka was an American professional football player, coach and commentator. During his playing career as a tight end, he was UPI NFL Rookie of Year in 1961, a five-time Pro Bowl selection, and a six-time All-Pro with the Chicago Bears, Philadelph
Nidal Malik Hasan is an American mass murderer, former United States Army major, and former physician before killing 13 people and injuring 32 others, on November 5, 2009.
Below is a 2026 Canadian supernatural thriller miniseries created by Jesse McKeown and starring Josh Hartnett, Mackenzie Davis, and Charlie Heaton. It premiered on Netflix on October 8, 2026. The series received generally positive reviews from critics.
Christa Gail Pike is an American convicted murderer on death row in Tennessee. In 1995, when she was 18, Pike and her boyfriend lured their 19-year-old classmate Colleen Slemmer into a wooded area in Knoxville where they tortured and killed her. Slemmer was be
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
Animals is a 2026 American crime thriller film directed by Ben Affleck and written by Affleck, Connor O. McIntyre and Billy Ray. It stars Affleck, Kerry Washington, Gillian Anderson, Adriana Paz with Luis Gerardo Méndez, and Steven Yeun. The film received nega
East of Eden (2026 miniseries)
East of Eden is a 2026 American historical drama television limited series created by Zoe Kazan, starring Florence Pugh. It is adapted from the 1952 novel by John Steinbeck and premiered on Netflix on October 1, 2026. It received generally positive reviews fro
Shane Michael Gillis is an American stand-up comedian, actor, and podcaster. He started performing stand-up in 2012, and in 2016, along with fellow stand-up comedian Matt McCusker, began co-hosting Matt and Shane's Secret Podcast, the most subscribed-to podcas
East of Eden is a 1952 family saga novel by American author and Nobel Prize winner John Steinbeck. Many regard the work as Steinbeck's most ambitious novel, and Steinbeck himself considered it his magnum opus. Steinbeck said of East of Eden, "It has everything
Other Mommy is a 2026 American supernatural psychological horror film directed by Rob Savage from a screenplay by Nathan Elston, based on the 2024 novel Incidents Around the House by Josh Malerman. The film stars Jessica Chastain as a mother who must do everyt
Lizzie Andrew Borden was an American woman who was tried and acquitted of the murders of her father and stepmother in Fall River, Massachusetts. No other individuals were charged with the murders.
Drishyam: The Conclusion is a 2026 Indian Hindi-language crime thriller film co-written, produced, and directed by Abhishek Pathak. Produced by Panorama Studios and Star Studio18, it is a sequel to Drishyam 2 (2022). Unlike its predecessors, the film features
Susan Lynn Lieberman Dell is an American businesswoman, philanthropist, and the wife of Michael Dell. They married in 1989, four years after Michael Dell formed his eponymous computer company. She is the co-founder and current chair of the Michael & Susan Dell
Digger is a 2026 American satirical black comedy film directed, co-written, and co-produced by Alejandro González Iñárritu. Tom Cruise, who also produced the film, stars as Digger Rockwell, an aging oil magnate whose drilling sets off an ecological disaster. T
Theodore John Kaczynski, also known as the Unabomber, was an American mathematician and domestic terrorist. A mathematics prodigy, he abandoned his academic career to pursue a reclusive primitive lifestyle and lone wolf terrorism campaign.
Lewis Terence Koumas is a professional footballer who plays as a forward for Premier League club Liverpool. Born in England, he plays for the Wales national team.
Ansel Easton Adams was an American landscape photographer and environmentalist known for his black-and-white images of the American West. He helped found Group f/64, an association of photographers advocating "pure" photography which favored sharp focus and th
Joshua Daniel Hartnett is an American actor. He began his career on ABC's drama series Cracker (1997–1998), after which he became known as a teen idol through starring parts in films such as Halloween H20: 20 Years Later, The Faculty, The Virgin Suicides (1999
2026 Formula One World Championship
The 2026 FIA Formula One World Championship is a motor racing championship for Formula One cars and the 77th running of the Formula One World Championship. It is recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of internati
Katie Zacharia is an American political commentator and attorney who has served as the White House press secretary since 2026.
Navanetham "Navi" Pillay is a South African jurist who served as the United Nations High Commissioner for Human Rights from 2008 to 2014. A South African of Tamil origin, Pillay was the first non-white female judge of the High Court of South Africa. She has al
Neatsville is an unincorporated community in Adair County, located 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
The term ancient lake has two different meanings. For biologists, an ancient lake has consistently carried water for a geologically significant amount of time, typically more than one million years. For paleontologists, an ancient lake is paleo-evidence of a l
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
The Social Reckoning is a 2026 American biographical legal thriller film written, co-produced, and directed by Aaron Sorkin. A follow-up to The Social Network (2010), it dramatizes the 2021 Facebook leak by the whistleblower Frances Haugen and Jeff Horwitz. It
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Policy design in experiments with unknown interference
This paper studies experimental designs for estimation and inference on policies with spillover effects. Units are organized into a finite number of large clusters and interact in unknown ways within each cluster. First, we introduce a single-wave experiment that estimates the marginal effect of a change in treatment probabilities, taking spillover effects into account. Using the marginal effect, we propose a policy
In computer vision, camera pose estimation from correspondences between 3D geometric entities and their projections into the image has been a widely investigated problem. Although most state-of-the-art methods exploit low-level primitives such as points or lines, the emergence of very effective CNN-based object detectors in the recent years has paved the way to the use of higher-level features carrying semantically m
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
This paper describes speaker verification (SV) systems submitted by the SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of fully supervised far-field speaker verification (Task 1) and semi-supervised far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and FFSVC2020 datasets as train datasets. An
Linearized Wasserstein dimensionality reduction with approximation guarantees
We introduce LOT Wassmap, a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. The algorithm is motivated by the observation that many datasets are naturally interpreted as probability measures rather than points in $\mathbb{R}^n$, and that finding low-dimensional descriptions of such datasets requires manifold learning algorithms in the Wasserstein space. Most availabl
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
We study stochastic Cubic Newton methods for solving general possibly non-convex minimization problems. We propose a new framework, which we call the helper framework, that provides a unified view of the stochastic and variance-reduced second-order algorithms equipped with global complexity guarantees. It can also be applied to learning with auxiliary information. Our helper framework offers the algorithm designer hi
StawGAN: Structural-Aware Generative Adversarial Networks for Infrared Image Translation
This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color images (NTIT2DC), which provide better perceptions of objects. We introduce a novel model that focuses on enhancing the quality of the target generation without merely colorizing it. The proposed structural aware (StawGAN) enables the translatio
Semantic Latent Decomposition with Normalizing Flows for Face Editing
Navigating in the latent space of StyleGAN has shown effectiveness for face editing. However, the resulting methods usually encounter challenges in complicated navigation due to the entanglement among different attributes in the latent space. To address this issue, this paper proposes a novel framework, termed SDFlow, with a semantic decomposition in original latent space using continuous conditional normalizing flow
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
The recent theoretical analysis of deep neural networks in their infinite-width limits has deepened our understanding of initialisation, feature learning, and training of those networks, and brought new practical techniques for finding appropriate hyperparameters, learning network weights, and performing inference. In this paper, we broaden this line of research by showing that this infinite-width analysis can be ext
X Hacking: The Threat of Misguided AutoML
Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as SHAP values. We show how easily an automated machine learning pipeline can be a
This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summarization dataset for related work generation, containing whole related work sections and full texts of cited papers. Its validation and test splits are constructed so that every source paper is available in full text, enabling controlled evaluation of full-text related w
TernaryLLM: Ternarized Large Language Model
Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements. Ternarization, an extreme form of quantization, offers a solution by reducing memory usage and enabling energy-efficient floating-point additions. However, applying ternarization to LLMs faces challenges stemming from outliers in both
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual propert
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are unknown, while only a control penalty function and constraints are provided. To this end, we embed state probability densities into a reproducing kernel Hilbert space (RKHS) to leverag
Machine-learning based high-bandwidth magnetic sensing
Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-spatial-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features. However, computing Shapley values exactly is expensive: for a general model with $n$ features, $O(2^n)$ model evaluations are necessary. To address this issue, approximation algorithms are widely used. One of the most popula
Instance-Level Difficulty: A Missing Perspective in Machine Unlearning
Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overlooking the varying difficulty of unlearning individual training samples. As a result, the broader feasibility of machine unlearning remains under-explored. This paper studies the cruxes that make machine unlearning difficult through a thorough instance-level unlearning pe
Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection
Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation proce
Deep End-to-End Survival Analysis with Temporal Consistency
In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learning principles, particularly the Deep Q-Network paradigm, extending Temporal Learning concepts to Survival Regression. A central argument in our method relies on temporal consistency, a recursive consistency condition between survival predic
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 diverse 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
Mechanisms of Multimodal Synchronization: Insights from Decoder-Based Video-Text-to-Speech Synthesis
Unified decoder-only transformers have shown promise for multimodal generation, yet how sequence design affects synchronization across modalities with heterogeneous sampling rates remains underexplored. We investigate this through video-text-to-speech (VTTS) synthesis, a controlled task requiring fine-grained temporal alignment between sparse text, video, and continuous speech. Using Visatronic, a unified decoder-onl
Understanding Transformer-based Vision Models through Inversion
Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promising yet only preliminarily explored approach is feature inversion via inverse networks, which reconstructs images from intermediate representations using trained inverse networks. In this study, we revisit feature inversion via inverse networks, introducing a novel, modular vari
In this work, we present Multiformer, a novel approach to depth-aware video panoptic segmentation (DVPS) based on the mask transformer paradigm. Our method learns object representations that are shared across segmentation, monocular depth estimation, and object tracking subtasks. In contrast to recent unified approaches that progressively refine a common object representation, we propose a hybrid method using task-sp
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script
DNN-based language models excel across various NLP tasks but remain highly vulnerable to textual adversarial attacks. While adversarial text generation is crucial for NLP security, explainability, evaluation, and data augmentation, related work remains overwhelmingly English-centric, leaving the problem of constructing high-quality and sustainable adversarial robustness benchmarks for lower-resourced languages both d
CRM: Retrieval Model with Controllable Condition
Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present the Controllable Retrieval Model (CRM), an outcome-conditioned retrieval framework that adds watch time as an input to the user tower. During training, CRM learns to predict the next item given its observed watch time; at inference, we supply a desired watch time to steer retr
Leveraging Motion Estimation for Efficient Bayer-Domain Video Convolutional Networks
Existing computer vision pipelines suffer from two efficiency bottlenecks: image signal processors (ISPs) first convert Bayer pixel data to RGB pixel by pixel, and video models repeatedly process temporally redundant frames, leading to high power consumption and latency. In this paper, we propose a novel framework that eliminates the ISP and leverages motion estimation to accelerate video vision tasks directly in the
Origins publishing files written for machines rather than people. Measured against a frozen cohort, so a change in the number means a change in adoption.
| Signal | Web head Tranco top 1,000 n=1,000 | Web tail sampled to rank 100k n=1,000 | AI-native model & dev platforms n=110 |
|---|---|---|---|
| Any agent-facing signal | 12.3% (123) | 4.7% (47) | 74.55% (82) |
| llms.txt (apex domain) | 9.9% (99) | 3.9% (39) | 60% (66) |
| llms.txt (docs subdomain) | 4.1% (41) | 1% (10) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai.txt | 0.2% (2) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.7% (7) | 0.1% (1) | 0.91% (1) |
Notable events recorded on this day and month across all years.