Record 04092026 · captured 2026-09-05
The world looked up Gloria Steinem. 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.
Gloria Marie Steinem was an American journalist and social movement activist who emerged as a nationally recognized leader of second-wave feminism in the United States in the late 1960s and early 1970s, and who remained particularly prominent in the 1980s and
Killing of the Clancy children
On January 24, 2023, Lindsay Clancy fatally strangled her three children—five-year-old Cora, three-year-old Dawson, and eight-month-old Callan—at the family's home in Duxbury, Massachusetts, United States. She then attempted suicide by cutting her wrists and n
David Charles Howard Bale was an English entrepreneur, environmentalist, and animal welfare activist. He was the father of actor Christian Bale and the husband of Gloria Steinem.
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian psychological thriller gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations respectively. It stars Yash, Kiara Advani,
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
Christian Charles Philip Bale is an English actor. Known for his versatility and physical transformations for his roles, he has been a leading man in films of several genres. His accolades include an Academy Award and two Golden Globe Awards, in addition to fo
Hanuman Ansh is a 2026 Indian Hindi-language devotional drama film written, directed and produced by Dr. Vishal Chaturvedi under his banner Swambhu Media Network. The film is based on his book Divine Detour: That Changed My Life, which chronicles the life of N
.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
Alphonso Smith Jr. was an American professional football player who was a cornerback for four seasons in the National Football League (NFL). Smith played college football for the Wake Forest Demon Deacons, and received consensus All-American honors. He was sel
Carla Jaran Jeffery was an American actress best known for playing the cheerleader Bree in Disney Channel's Zombies film franchise.
Dolly Rebecca Parton Dean was an American singer-songwriter, actress and businesswoman. Dubbed the "Queen of Country", Parton was one of the most successful country music performers in history. She was also known for her cultural influence and philanthropy, pa
Christopher Langham was an English writer, actor and comedian. He was known for playing the cabinet minister Hugh Abbot in the BBC sitcom The Thick of It, and as presenter Roy Mallard in People Like Us, first on BBC Radio 4 and later on its transfer to televis
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
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
Neem Karoli Baba also known as Neeb Karori Baba and by his followers as Maharaj-ji, was a Hindu guru and devotee of the Hindu deity Hanuman. He was born "Laksman Narayan Sharma" around c. 1900 and died on 11 September 1973.
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 Gentlemen (2024 TV series)
The Gentlemen is an action comedy television series created by Guy Ritchie for Netflix and is a spin-off of Ritchie's 2019 film. The series stars Theo James in the lead role and premiered on March 7, 2024. In August 2024, the series was renewed for a second se
Timothy James Curry was an English actor and singer who amassed more than 240 credits on screen and stage in a career which spanned nearly 60 years. Curry made his acting debut in the West End production of Hair (1968–1969) and gained wide recognition for port
Coyote vs. Acme is a 2026 American live-action animated legal comedy film directed by Dave Green and written by Samy Burch, who developed the story with James Gunn and Jeremy Slater. Based on the 1990 The New Yorker magazine article "Coyote v. Acme" by Ian Fra
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
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
Cassandra Wilson was an American jazz singer, songwriter and producer from Jackson, Mississippi. She was one of the most successful female jazz singers and has been described by critic Gary Giddins as "a singer blessed with an unmistakable timbre and attack [w
The Whisper Man is a 2026 American crime thriller film directed by James Ashcroft and written by Ben Jacoby and Chase Palmer. It is based on the novel of the same name by Alex North and stars Robert De Niro, Michelle Monaghan, and Adam Scott.
In mathematics, the RSA numbers are a set of large semiprimes that were part of the RSA Factoring Challenge. The challenge was to find the prime factors of each number. It was created by RSA Laboratories in March 1991 to encourage research into computational n
Maria Sara Bartiromo is an American conservative journalist and author who has also worked as a financial reporter and news anchor. She was the host of Mornings with Maria and Maria Bartiromo's Wall Street on the Fox Business channel, and Sunday Morning Future
Buddy is a 2026 American black comedy supernatural horror film directed by Casper Kelly and written by Kelly and Jamie King. The film stars Cristin Milioti, Delaney Quinn, Patton Oswalt, Clint Howard, Michael Shannon, Topher Grace, and Keegan-Michael Key as th
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").
Constance Margaret Fisher was an American serial mass murderer. Diagnosed with paranoid schizophrenia, she killed three of her children in Maine in 1954, and after spending several years in a mental institution, she was released, only to kill three more of her
The Black List is an annual survey of the "most-liked" motion picture screenplays not yet produced. It has been published every year since 2005 on the second Friday of December by Franklin Leonard, a development executive who has worked at Universal Pictures a
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and validated against real data. Conventional methods approach these tasks by assessing the model-data match via simple hypothesis tests or distance minimization in an ad hoc fashion, but they can encounter challenges arising from non-identifiab
Evaluating Proposed Fairness Models for Face Recognition Algorithms
The development of face recognition algorithms by academic and commercial organizations is growing rapidly due to the onset of deep learning and the widespread availability of training data. Though tests of face recognition algorithm performance indicate yearly performance gains, error rates for many of these systems differ based on the demographic composition of the test set. These "demographic differentials"
Anisotropic View Distance Metric for High-Dimensional Data: Theory, Geometry, and Fast Computation
K-Means clustering algorithm is one of the most commonly used clustering algorithms because of its simplicity and efficiency. K-Means clustering algorithm based on Euclidean distance only pays attention to the linear distance between Euclidean distance is an efficient and interpretable similarity measurement, but its effectiveness may deteriorate in sample spaces with anisotropic structures, redundant features, or co
On the Equality of the ELBO to a Sum of Entropies at Stationary Points of Learning
The variational lower bound (a.k.a. ELBO or free energy) is the central objective for many established as well as for many novel algorithms for unsupervised learning. Such algorithms usually increase the bound until parameters have converged to values close to a stationary point of the learning dynamics. Here we show that (for a very large class of generative models) the variational lower bound is at all stationary p
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations
Progress in graph neural networks has grown rapidly in recent years, with many new developments in drug discovery, medical diagnosis, and recommender systems. While this progress is significant, many networks are `black boxes' with little understanding of the `what' exactly the network is learning. Many high-stakes applications, such as drug discovery, require human-intelligible explanations from the models s
Data Market Design through Deep Learning
The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al., 2018]. Each buyer has their own decision to make in a world environment, and their subjective expected value for the in
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Purpose: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal-to-noise ratio, and good human interpretability
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
The generalization error curve of certain kernel regression method aims at determining the exact order of generalization error with various source condition, noise level and choice of the regularization parameter rather than the minimax rate. In this work, under mild assumptions, we rigorously provide a full characterization of the generalization error curves of the kernel gradient descent method (and a large class o
Early detection of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration depends on retinal fundus image analysis, but manual assessment is slow and expert-dependent. Automated convolutional neural networks classify fundus images accurately yet act as black boxes, and existing retinal vessel segmentation methods lose discriminative power under pathology and seldom
Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate this problem by greedily restricting what tokens an LLM can output at each step to guarantee that the output matches a given constraint. Specifically, in grammar-constrained decoding (GCD), the LLM's output must follo
Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading
Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the training of transformers is very expensive and often hits a ``memory wall'', i.e., even when using 3D parallelism (pipeline, tensor, data) and aggregating the memory of many GPUs, it is still not enough to hol
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising collaborative learn
Detecting Conversational Mental Manipulation with Intent-Aware Prompting
Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach
LDC: Learning to Generate Research Idea with Dynamic Control
Recent advancements in large language models (LLMs) have demonstrated their potential in automating the scientific research ideation. Existing approaches primarily focus on prompting techniques, often producing ideas misaligned with expert standards - novelty, feasibility, and effectiveness, which are widely recognized by the research community as the three key subdimensions of high-quality ideas. Also, balancing the
The current study investigates possible neural mechanisms underling autonomous shifts between focus state and mind-wandering by conducting model simulation experiments. On this purpose, we modeled perception processes of continuous sensory sequences using our previous proposed variational RNN model which was developed based on the free energy principle. The current study extended this model by introducing an adaptati
AgentRM: Enhancing Agent Generalization with Reward Modeling
Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability. In this work, we find that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model. Based on this finding, we prop
Reward Shaping to Mitigate Reward Hacking in RLHF
Reinforcement learning from human feedback (RLHF) is widely used to align large language models (LLMs) with human preferences. However, RLHF remains vulnerable to \emph{reward hacking}, whereby a policy exploits imperfections in the reward function instead of learning the intended behavior, thereby undermining alignment. Although reward shaping can stabilize RLHF training and partially mitigate reward hacking, shapin
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain-specific knowledge, motivating the development of a Graph Foundation Model (GFM) that generalizes across diverse graphs and tasks. Despite large efforts to integrate Large Language Models (LLMs) and Graph Neural Networks (GNNs) for TAGs, e
Automatic visual inspection using machine learning plays a key role in achieving zero-defect policies in industry. Research on anomaly detection is constrained by the availability of datasets that capture complex defect appearances and imperfect imaging conditions, which are typical of production processes. Recent benchmarks indicate that most publicly available datasets are biased towards optimal imaging conditions,
Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems
The application of learning based methods to vehicle routing problems has emerged as a pivotal area of research in combinatorial optimization. These problems are characterized by vast solution spaces and intricate constraints, making traditional approaches such as exact mathematical models or heuristic methods prone to high computational overhead or reliant on the design of complex heuristic operators to achieve opti
Bayesian Network Structural Consensus via Greedy Min-Cut Analysis
This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial unrestricted fusion using a structural score based on min-cut analysis, integrated into a modified Backward Equivalence Search (BES) phase of the Greedy Equivalence Searc
Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift
We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data. This problem is central to decision-making under distribution shift, where treatment patterns may change at deployment. We develop a semiparametric framework for counterfactual regression along a prespecified incremental-intervention path. The target is a finite-dimensional con
Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Traditional statistical approaches often struggle to capture complex non-linear relationships, while many machine learning methods overlook the joint roles of spatial autocorrelation and network topology in urban systems. Recent advances in Ge
Behavior of prediction performance metrics with rare events
Objective: Area under the receiving operator characteristic curve (AUC) is commonly reported alongside prediction models for binary outcomes. Recent articles have raised concerns that AUC might be a misleading measure of prediction performance in the rare event setting. This setting is common since many events of clinical importance are rare. We aimed to determine whether the bias and variance of AUC are driven by th
Sionna is an open-source, GPU-accelerated library that, as of version 0.14, incorporates a ray tracer, Sionna RT, for simulating radio wave propagation. A unique feature of Sionna RT is differentiability, enabling the calculation of gradients for the channel impulse responses (CIRs), radio maps, and other related metrics with respect to system and environmental parameters, such as material properties, antenna pattern
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 | 11.7% (117) | 4.3% (43) | 75.45% (83) |
| llms.txt (apex domain) | 9.4% (94) | 3.6% (36) | 60% (66) |
| llms.txt (docs subdomain) | 3.8% (38) | 1% (10) | 43.64% (48) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 7.27% (8) |
| .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) | 0.6% (6) | 0.1% (1) | 0.91% (1) |
Notable events recorded on this day and month across all years.