Record 25052026 · captured 2026-08-25
The world looked up Obsession (2025 film). 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.
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
Kyle Thomas Busch, nicknamed "Rowdy", was an American professional stock car racing driver and racing team owner who competed from 2001 until his death in 2026. Throughout his career, Busch raced under several car numbers, though he was most prominently identi
Murder of Dominic Russo and Davion Flanagan
The murder of Dominic Russo and Davion Flanagan occurred during the early morning hours of July 31, 2022, when Mackenzie Shirilla intentionally crashed her vehicle into a brick wall in Strongsville, Ohio, United States, killing two passengers: her boyfriend, D
Star Wars: The Mandalorian and Grogu is a 2026 American science fiction film directed by Jon Favreau, who co-wrote the film with Dave Filoni and Noah Kloor. Produced by Lucasfilm and Fairview Entertainment, and distributed by Walt Disney Studios Motion Picture
Pentecost is a Christian holiday that takes place on the 49th day after Easter. It commemorates the descent of the Holy Spirit upon the Apostles of Jesus, Mary, and other followers of Christ, while they were in Jerusalem celebrating the Feast of Weeks, as desc
The Boroughs is an American science fiction television series created by Jeffrey Addiss and Will Matthews and executive produced by The Duffer Brothers.
Ricardo "Rico" Verhoeven is a Dutch professional kickboxer, professional boxer and mixed martial artist. He formerly competed in the Heavyweight division of GLORY, where he was the promotion's longest-reigning Heavyweight Champion, while also defending the tit
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
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
On May 21, 2026, a chemical leak occurred at a GKN Aerospace manufacturing facility in Garden Grove in Orange County, California, about 35 miles (56 km) southeast of Los Angeles. First responders with the Orange County Fire Authority (OCFA) determined that a c
Cyrus Leland Sr. was a lawyer from Sauk City, Wisconsin and Troy, Kansas who served a single one-year term in the Wisconsin State Assembly representing Sauk County as a Democrat; and served as a colonel in the Union Army during the American Civil War.
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Drishyam 3 is a 2026 Indian Malayalam-language crime thriller film written and directed by Jeethu Joseph. Produced by Antony Perumbavoor for Aashirvad Cinemas, it is a sequel to Drishyam 2 (2021) and the third installment in the Drishyam film series. The film
Oleksandr Oleksandrovych Usyk is a Ukrainian professional boxer. He was the undisputed world heavyweight champion twice in 2024 and 2025 and held the unified heavyweight championship from 2021 until vacating his sanctioning body titles in June 2026, and has he
Saturday Night's Main Event is a series of American professional wrestling television specials produced by WWE. The series originally broadcast from 1985 to 1992, by NBC until 1991 then briefly by Fox. The specials were briefly revived on NBC from 2006 to 2008
Bettina Elisabeth Anderson is an American socialite and model. She is married to Donald Trump Jr. and is the daughter-in-law of United States president Donald Trump.
The Cockroach Janta Party, also known as the Cockroach movement, is an Indian youth-based satirical political movement founded on 16 May 2026 by Abhijeet Dipke, a political communications strategist and activist. The CJP harnessed widespread political and econ
David Joseph Malukas is an American racing driver who competes in the IndyCar Series driving the No. 12 Chevrolet for Team Penske. He previously drove for A.J. Foyt Racing in 2025, Meyer Shank Racing in 2024, and Dale Coyne Racing in 2022 and 2023.
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
Karl Felix Helmer Rosenqvist is a Swedish professional racing driver drives the No. 60 Honda for Meyer Shank Racing (MSR) in the IndyCar Series. He was named the 2019 IndyCar Rookie of the Year and won the 2026 Indianapolis 500.
The 2025–26 Premier League was the 34th season of the Premier League and the 127th season of top-flight English football. The fixtures were released on 18 June 2025. The season consisted of 33 weekend and five midweek rounds of matches.
.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
Kylie Ann Minogue is an Australian singer, songwriter, and actress. Frequently referred to as the "Princess of Pop", she has achieved recognition in both the music industry and the fashion world as a major style icon. Her accolades include two Grammy Awards, f
Ella Bright is an American-British actress and singer. She began acting as a child. For her performance in the CBBC adaptation of Malory Towers (2020–2025), she received Children's BAFTA and Emmy Award nominations. She has since starred in the Prime Video seri
Josep "Pep" Guardiola Sala is a Spanish football manager and former player from Catalonia. He is the global ambassador of the City Football Group and was most recently the manager of Premier League club Manchester City. Widely regarded as one of the greatest f
Memorial Day is a federal holiday in the United States for mourning the U.S. military personnel who died while serving in the U.S. Armed Forces. It is observed on the last Monday of May.
The 2026–27 UEFA Europa League is the 56th season of Europe's secondary club football tournament organised by UEFA, and the 18th season since it was renamed from the UEFA Cup to the UEFA Europa League.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Machine-Assisted Script Curation
We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring. Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events; (3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events. MASC automates portions of the s
Deep energy renovation of building stock came more into focus in the European Union due to energy efficiency related directives. Many buildings that must undergo deep energy renovation are old and may lack design/renovation documentation, or possible degradation of materials might have occurred in building elements over time. Thermal transmittance (i.e. U-value) is one of the most important parameters for determining
Perhaps PTLMs Should Go to School -- A Task to Assess Open Book and Closed Book QA
Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings. We propose a new task that includes tw
Remember what you did so you know what to do next
We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforcement learning. Using the Markov assumption (a single previous
Moonwalk: Inverse-Forward Differentiation
Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of vector-Jacobian products, an operation that is g
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
Post-hoc out-of-distribution (OOD) detection has garnered intensive attention in reliable machine learning. Many efforts have been dedicated to deriving score functions based on logits, distances, or rigorous data distribution assumptions to identify low-scoring OOD samples. Nevertheless, these estimate scores may fail to accurately reflect the true data density or impose impractical constraints. To provide a unified
RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization
Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks with efficient decoding. Hybrid NeRV methods further improve reconstruction quality through content adaptive embeddings, but still struggle to preserve fine details at low bitrates. A key limitation is that shallow residual support in formation, although highly beneficia
Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries
Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and learning problems. This difficulty arises from several factors, including the exponential dimensionality of the Hilbert spaces and the information loss in quantum measurements. Existing estimators, such as finite difference and the parameter shift rule, often fail to adequately ad
Nonlinear Transformations Against Unlearnable Datasets
Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studies have begun to tackle the privacy concerns associated with this data collection method. Notable approaches include Deepconfuse, error-minimizing, error-maximizing (also known as adversarial poisoning), Neural Tangent Generalization Attack, synthetic, autoregressive, One-
BVI-RLV: A Fully Registered Dataset for Low-Light Video Enhancement
Low-light videos often exhibit spatiotemporally incoherent noise, compromising visibility and degrading performance in computer vision applications. A major challenge for enhancing such content using deep learning lies in the scarcity of pixel-aligned, high-quality training data. We introduce BVI-RLV, a fully registered low-light video dataset comprising over 30k paired frames from 40 diverse scenes under two low-lig
Vector Retrieval with Similarity and Diversity: How Hard Is It?
Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search to retrieval-augmented generation and knowledge-intensive reasoning. Beyond retrieving items that are individually similar to a query, many applications require a set of results that is also diverse, complementary, and collectively informative. Balancing similarity and diversi
Universal Matrix Multiplication on Quantum Computer
As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inference performance of models. Harnessing the parallel processing capabilities afforded by quantum superposition and entanglement to reshape matrix multiplication implementations has become a promising entry point for optimising underlying quantu
Heterogeneous Sheaf Neural Networks
Heterogeneous graphs, whose nodes and edges can belong to different types and feature spaces, arise in many real-world domains, including biology, recommendation, social networks, and computer systems. Existing heterogeneous graph neural networks typically handle this heterogeneity at the architectural level through relation-specific modules, meta-path machinery or type-aware attention, which often leads to increasin
Contrastive learning yields impressive results for self-supervision in computer vision. The approach relies on the creation of positive pairs, something which is often achieved through augmentations. However, for multivariate time series effective augmentations can be difficult to design. Additionally, the number of input channels for biosignal datasets often varies from application to application, limiting the usefu
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
We introduce the CRONOS algorithm for convex optimization of two-layer neural networks. CRONOS is the first algorithm capable of scaling to high-dimensional datasets such as ImageNet, which are ubiquitous in modern deep learning. This significantly improves upon prior work, which has been restricted to downsampled versions of MNIST and CIFAR-10. Taking CRONOS as a primitive, we then develop a new algorithm called CRO
A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing
We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, including the optimal one, may be entirely unobserved. This setting is common in practice and is especially difficult in dynamic environments. Existing offline reinforcement learning methods typically rely on full or partial coverage and can therefore perform poorly in such settings. W
SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which limits its practical applications. To achieve explainability, decision trees have emerged as a popular and promising alternative to neural networks. Nonetheless, due to their limited expressiveness, traditional decision trees struggle with hig
SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation
Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding task interference. We argue that its greater potential lies in enabling task synergy, where tasks actively improve one another. We identify cross-task performance, defined by compatibility between encoders and predictors across tasks, as a key indicator of merge quality. We
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinformation via deepfake audio. Watermarking offers a proactive solution by embedding imperceptible yet identifiable and traceable signals into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have
TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives
TerraQ is a spatiotemporal question-answering engine for satellite image archives. It is a natural language processing system that is built to process requests for satellite images satisfying certain criteria. The requests can refer to image metadata and entities from a specialized knowledge base (e.g., the Emilia-Romagna region). With it, users can make requests like "Give me a hundred images of rivers near port
Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast convergence rates -- remains challenging. A common approach to address this is targeted regularization, which modifies the objective function of NNs. However, existing work on neural causal effect estimation is largely limited to continuou
State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. While ordinary differential equations (ODE) are the prevalent models in science and engineering, a baseline model that forecasts a constant value outperforms ODE-based models from the last five years on three of these existing datasets. This
Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations
Robust Counterfactual Inference in Markov Decision Processes
This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make counterfactuals identifiable. However, there are usually many causal models that align with the observational and interventional distributions of an MDP, each yielding different counterfactual distributions, so fixing a particular causal mod
Diffusion and Flow Matching Models for Tabular Data: A Survey
Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, generative modeling remains difficult: a dataset may contain numerical and categorical attributes, missing values, sensitive fields, imbalanced categories, complex feature dependencies, and domain constraints. Earlier tabular data modeling me
Notable events recorded on this day and month across all years.