Record 28102026 · captured 2026-10-29
The world looked up 2026 Israeli legislative election. 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.
2026 Israeli legislative election
Legislative elections were held in Israel on 27 October 2026 to elect the 120 members of the 26th Knesset and determine the governing coalition that succeeds the 37th government led by Prime Minister Benjamin Netanyahu.
Harry Edward Kane is an English professional footballer who plays as a striker for Bundesliga club Bayern Munich and captains the England national team. Known for his clinical finishing, passing, vision and tactical versatility, he is widely regarded as one of
Susan Saint James was an American actress. In 1968, she landed a role as Peggy Maxwell on the TV series The Name of the Game. Saint James received three Emmy Award nominations for the series, winning once in 1969. She is also known for her appearances as Sally
The Ballon d'Or is an annual association football award presented by French magazine France Football since 1956 to honour the player judged to have performed the best over the previous season.
The 2026 Ballon d'Or was the 70th annual ceremony of the Ballon d'Or, presented by France Football, recognising the best footballers in the world in the 2025–26 season. For the fifth time in the history of the award, it was given based on the results of the se
Walter Marvin Koenig was an American actor and screenwriter. He began acting professionally in the mid-1960s and quickly rose to prominence for his supporting role as Ensign Pavel Chekov in Star Trek (1967–1969). He went on to reprise this role in all six orig
Gadi Eisenkot, also spelled Eizenkot, is an Israeli general, politician, and leader of the Yashar party. He served from 2015 to 2019 as the 21st chief of staff of the Israel Defense Forces (IDF) and from 2023 to 2024 as a minister without portfolio in Israel's
Eric Bauza was a Canadian voice actor and animator. He was born in Toronto, Ontario and later moved to Los Angeles, California.
Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar. It was produced by Kalanithi Maran through Sun Pictures. A sequel to Jailer (2023), the film stars Rajinikanth, Ramya Krishnan, Yogi Babu, Mirnaa and Rithvik
Alèxia Putellas i Segura, often known simply as Alexia, is a Spanish professional footballer from Catalonia who plays as a midfielder or forward for the Women's Super League club London City Lionesses and the Spain women's national team. She previously played
Onika Tanya Maraj-Petty, known professionally as Nicki Minaj, is a Trinidadian rapper, singer, and songwriter. Regarded as the "Queen of Rap" and one of the most influential rappers of all time, she is noted for her dynamic rap flow, witty lyrics, musical vers
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
Elizabeth Anne Holmes is an American businesswoman convicted of fraud in connection with her health technology company Theranos. Holmes founded Theranos in 2003, and its valuation soared in the early 2010s after it claimed to have revolutionized blood testing
Tanner Jeremie McKee is an American professional football quarterback for the Philadelphia Eagles of the National Football League (NFL). He played college football for the Stanford Cardinal and was selected by the Eagles in the sixth round of the 2023 NFL draf
Clayface is a 2026 American body horror film based on the eponymous character from DC Comics. Directed by James Watkins from a screenplay by Mike Flanagan and Hossein Amini, it is the third film in the DC Universe (DCU). Tom Rhys Harries stars as Matt Hagen /
You Can See Everything is a 2026 American meta-documentary film directed by Nathan Fielder and Lance Oppenheim. It follows Elizabeth Holmes, the founder of the defunct health technology company Theranos, and her partner Billy Evans, who invited a film crew to
Luiz Inácio Lula da Silva, known mononymously as Lula, is a Brazilian politician, trade unionist, and former metalworker who has served as the 39th president of Brazil since 2023. A member of the Workers' Party, he served as the 35th president from 2003 to 201
Dominic Ierace, known professionally as Donnie Iris, was an American rock musician known for his work with the Jaggerz and Wild Cherry during the 1970s and for his solo career beginning in the 1980s with his band, the Cruisers. He wrote the No. 2 Billboard hit
Fleetwood Town Football Club is a professional association football club based in Fleetwood, Lancashire, England. The team competes in EFL League Two, the fourth level of the English football league system.
Opinion polling for the 2026 Israeli legislative election
In the run up to the 2026 Israeli legislative election, various organisations conducted opinion polling to gauge voting intention in Israel during the term of the twenty-fifth Knesset. This article lists the results of such polls. The date range for these opin
Benjamin "Bibi" Netanyahu is an Israeli politician and diplomat who has served as prime minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Yashar, stylized as Yashar! with Eisenkot, is a centrist political party in Israel led by Gadi Eisenkot. Founded in September 2025 by Eisenkot, the former Chief of Staff of the Israel Defense Forces and National Unity Knesset member, the party contested the 20
Wildwood is a 2026 American stop-motion animated dark fantasy film directed by Travis Knight and written by Chris Butler, based on the 2011 novel by Colin Meloy and Carson Ellis. Produced by Laika, the film stars Carey Mulligan, Peyton Elizabeth Lee, Jacob Tre
Jair Messias Bolsonaro is a Brazilian politician and former military officer who served as the 38th president of Brazil from 2019 to 2023. He previously served as a member of the Chamber of Deputies from 1991 to 2019.
In computational complexity theory, the exponential time hypothesis or ETH states that satisfiability of 3-CNF Boolean formulas (3-SAT) cannot be solved in subexponential time, . More precisely, the usual form of the hypothesis asserts the existence of a numbe
2026 Serbian parliamentary election
Early parliamentary elections were held in Serbia on 25 October 2026 to elect members of the National Assembly. The elections were originally due by December 2027; Aleksandar Vučić, then the president of Serbia, dissolved the National Assembly and called the e
2026 Brazilian general election
General elections were held in Brazil on 4 October 2026 to elect the president, vice president, members of the National Congress, the governors, vice governors and legislative assemblies of all federative units, and the district council of Fernando de Noronha.
Matthew Langford Perry was an American and Canadian actor. Known primarily for his work in comedy, he gained international fame for starring as Chandler Bing on the NBC television sitcom Friends (1994–2004), receiving his first Primetime Emmy Award nomination
Duncan Richard Ebersol is an American television executive and a senior adviser for NBC Universal Sports & Olympics. He had previously been the chairman of NBC Sports, producing large-scale television events such as the Olympic Games and National Football Leag
Flávio Nantes Bolsonaro is a Brazilian lawyer and conservative politician serving as a senator for Rio de Janeiro since 2019. A son of the 38th president of Brazil, Jair Bolsonaro, he is affiliated with the Liberal Party (PL). He previously served as a state d
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization
Real-time and online action localization in a video is a critical yet highly challenging problem. Accurate action localization requires the utilization of both temporal and spatial information. Recent attempts achieve this by using computationally intensive 3D CNN architectures or highly redundant two-stream architectures with optical flow, making them both unsuitable for real-time, online applications. To accomplish
NAG-GS: A Semi-Implicit Stochastic Optimizer with Large-Step Stability
Stochastic first-order methods are widely used for large-scale optimization and machine learning, where their performance often depends on the learning rate. We study NAG-GS, a stochastic momentum method based on a semi-implicit Gauss--Seidel discretization of a Nesterov-type flow. For strongly convex quadratics, we determine the exact range of stable step sizes for fixed damping and the step size that minimizes the
Epsilon-Identifiability of Causal Quantities
Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the available data sources. This paper shows how approximate identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain
From system models to class models: An in-context learning paradigm
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this paper. In response to this query, we introduce a novel paradigm for system identification, addressing two primary tasks: one-step-ahead prediction and multi-step simulation. Unlik
Patterns of microcircuitry in the cerebral cortex suggest that the brain has an array of repeated canonical computational units. However, neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. Here we integrate normative and algorithmic theories of neural c
DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors
Achieving constant accuracy in object detection is challenging due to the inherent variability of object sizes. One effective approach to this problem involves optimizing input resolution, referred to as a multi-resolution strategy. Previous approaches to resolution optimization have often been based on pre-defined resolutions with manual selection. However, there is a lack of study on run-time resolution optimizatio
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they
Adaptive Momentum Benefits Score-based Generative Models
Score-based Generative Models (SGMs) have achieved remarkable success in generation tasks by establishing a diffusion process that gradually perturbs real data into Gaussian noise and then learning a reverse process to reconstruct the data from noise. However, conventional denoising methods, such as Langevin dynamics and numerical stochastic differential equation solvers, rely on randomness but suffer from slow gener
We discuss a connection between a generative model, called the diffusion model, and nonequilibrium thermodynamics for the Fokker-Planck equation, called stochastic thermodynamics. Using techniques from stochastic thermodynamics, we derive the speed-accuracy relations for diffusion models, which are inequalities that relate the accuracy of data generation to the entropy production rate. This relation can be interprete
We study a multi-round mechanism design problem, where we interact with a set of agents over a sequence of rounds. We wish to design an incentive-compatible (IC) online learning scheme to maximize an application-specific objective within a given class of mechanisms, without prior knowledge of the agents' type distributions. Even if each mechanism in this class is IC in a single round, if an algorithm naively choo
Fairness Definitions in Language Models Explained
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However,
She Works, He Works: A Curious Exploration of Gender Bias in AI-Generated Imagery
She Works, He Works is an interdisciplinary project that combines feminist visual theory, AI critique, and artistic practice to examine gender representation in AI-generated imagery. By juxtaposing text-to-image outputs of male and female construction workers, it reveals how societal biases manifest in generative AI systems. The study employs critically crafted prompts that blend traditionally gendered traits with un
In this paper, we compare the performance of three popular deep learning architectures: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and CNN-LSTM in water level prediction over different time window sizes. We used wind, pressure, and tide data from National Oceanic and Atmospheric Administration (NOAA) stations. Our results show that the LSTM model generally outperformed the other models in for
A Deep Learning Framework for TTV Characterization: A Case Study of the Kepler-88 System
Transit timing variation (TTV) provides rich information about the mass and orbital properties of exoplanets, which are often obtained by solving an inverse problem via Markov Chain Monte Carlo (MCMC). In this paper, we design a new data-driven approach, which potentially can be applied to problems that are hard to traditional MCMC methods, such as the case with only one planet transiting. Specifically, we use a deep
A Unified Approach to Routing and Cascading for LLMs
The availability of a wide range of large language models (LLMs) embedded in various agentic systems has significantly increased the potential of model selection strategies to improve the cost-performance tradeoff. Existing strategies involve either routing, where a single model is chosen per query, or cascading, which sequentially runs increasingly larger models until a satisfactory answer is found. However, current
Hate speech detectors must separate hateful posts from the far more common offensive but non-hateful ones, and two popular ideas for doing so are hierarchical (coarse-to-fine) classification and sentiment features. We revisit our earlier transformer-based framework, which combines both ideas, and test each component in a controlled study on two public benchmarks (Davidson et al., 24,783 tweets; HateXplain, 19,229 pos
Temporal difference learning adapts to incomplete coverage: an oracle inequality over subgraphs
Temporal difference (TD) and Monte Carlo (MC) are two fundamental principles for value function estimation in reinforcement learning. TD is asymptotically efficient for value estimation, whereas MC requires only adequate target visitation. We show that these advantages are automatically combined by ordinary TD in the tabular setting. We identify the subgraph Bellman variance, arising from bootstrapping within a subgr
The Price of Failure Risk: Clipped Barrier Policy Gradients with Convex Regularization
Many operations run on a budget that must rarely run out, such as a wear allowance. A wrapper keeps the remaining budget in the state, subtracts a barrier that grows as the budget shrinks and clips the combined reward from below at -m, and a standard policy-gradient learner trains unchanged. The clip level m is a price of failure risk, since the trained value splits exactly into utility before failure, the cost of a
Event-triggered recording reduces the footage a surveillance camera must store, but the trigger decides how much idle video is kept, how many events are missed, and how much computation is spent. We compare six motion triggers (frame differencing, dense optical flow, MOG2, KNN, CNT, GSOC), a CNN detector, strided detection, and three motion-gated hybrids under one recording policy, on a benchmark of seven static-came
DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization
Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses often require access to the model internals or additional training, limiting their applicability for service providers deployed through black-box APIs. In this paper, we propose DYNASHIELD, a moving target defense framework that improves robustness by customizing decoding hyperpar
Anomaly Detection via Autoencoder Composite Features and NCE
Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for
Correcting Before Adapting: Gradient Correction for Federated Adaptive Optimization
Client-drift compensation methods mitigate the impact of data heterogeneity in federated learning (FL). While drift correction is well studied for stochastic gradient descent (SGD)-based FL, its interaction with adaptive client optimizers remains less understood. Adaptive optimizers such as Adam build each update from running averages of past gradients, so correcting only the final update direction leaves those avera
Targeted Learning for Inference on Data Fairness
As algorithmic decision making has become increasingly ubiquitous even in high-stakes settings, significant effort has been invested in defining, detecting, and eliminating unfair model-driven outcomes. Prior work has largely focused on estimating the fairness properties of a given predictive model, and quantifying uncertainty based on variability in a fixed model's performance across evaluation datasets. Alterna
Non-asymptotic Estimation of Sparse Partial Correlation via Joint Partial Regression
Partial correlation analysis is widely used in modern multivariate analysis. However, prior methods lack non-asymptotic guarantees and have slow asymptotic estimation rates. We present a method for simultaneously estimating sparse high-dimensional partial correlation matrices and inverse covariance with non-asymptotic rates. The method is a two-stage estimation which exploits the connection between the inverse covari
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inhere
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 | 13.5% (135) | 5.6% (56) | 74.55% (82) |
| llms.txt (apex domain) | 11.4% (114) | 4.7% (47) | 60.91% (67) |
| llms.txt (docs subdomain) | 4.3% (43) | 1% (10) | 45.45% (50) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.3% (3) | 0.91% (1) |
Notable events recorded on this day and month across all years.