Record 28082026 · captured 2026-08-29
The world looked up Tim Curry. 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.
Timothy James Curry was an English actor and singer. In a career that spanned nearly 60 years, he was noted for his performances across theatre, cinema, television, and animation.
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 via
Peter Claver Cullen was a Canadian voice actor. He voiced Optimus Prime in the original 1980s Transformers animated series, later returning to the role in Transformers media in 2007, starting with the first live-action film. He also voiced many other character
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations LLP respectively. It stars Yash in the lead role alongside Kiara
Lake Ontario is one of the five Great Lakes of North America. It is bounded on the north, west, and southwest by the Canadian province of Ontario, and on the south and east by the U.S. state of New York. The Canada–United States border spans the centre of the
Yayoi Kusama was a Japanese contemporary artist who worked primarily in sculpture and installation. She was also active in painting, performance, video art, fashion, poetry, fiction, and other arts. Her work was based in conceptual art and shows some attribute
On the morning of 26 August 2026, a series of flash floods and mudslides along a 72 km (45 mi) stretch of the Trishuli River struck dozens of settlements in Nepal's Rasuwa, Nuwakot, Dhading, Gorkha, Tanahun, Nawalparasi, and Chitwan districts. It completely de
Killing of the Clancy children
On January 24, 2023, Lindsay Clancy strangled her three children, five-year-old Cora, three-year-old Dawson, and eight-month-old Callan, at their home in Duxbury, Massachusetts, United States. She then attempted suicide by cutting her wrists and neck and jumpi
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
Ratko Mladić was a Bosnian Serb military officer who led the Army of Republika Srpska (VRS) during the Yugoslav Wars. Referred to as the "Butcher of Bosnia", he was found guilty in 2017 of committing war crimes, crimes against humanity, and genocide by the Int
The Rocky Horror Picture Show is a 1975 musical comedy film produced by Lou Adler and Michael White, directed by Jim Sharman, and distributed by 20th Century Fox. The film is based on the 1973 musical stage production The Rocky Horror Show, with music, book, a
The 2026–27 UEFA Champions League is the 72nd season of Europe's premier football tournament organised by UEFA and the 35th season since it was rebranded from the European Cup to the UEFA Champions League.
Samuel Monroe Jr. was an American actor and rapper under the name Caffeine.
List of Tim Curry performances
Timothy James Curry (1946–2026) was an English actor and singer based in the United States who had several roles in video games, films, television shows, and audiobooks.
Hayden Lesley Panettiere was an American actress and singer. She first appeared on-screen in a commercial in 1990 when she was 11 months old, and by age five, she had appeared in more than 50 commercials. Her first major television roles were as Sarah Roberts
Gayatri Das, better known by her stage name Geetu Mohandas, is an Indian actress and director known for her works in Malayalam cinema. In 2013, she directed the socio political film Liar's Dice which has received two National Film Awards, was premiered at Sund
Nepal, officially the Federal Democratic Republic of Nepal, is a landlocked country in South Asia. It is mainly situated in the Himalayas, but also includes parts of the Indo-Gangetic Plain. It borders the Tibet Autonomous Region of China to the north, and Ind
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
Assassination of Lord Mountbatten
Lord Mountbatten, a retired British statesman and cousin to Queen Elizabeth II, was assassinated on 27 August 1979 off the coast of Mullaghmore, Ireland. Thomas McMahon, an Irish republican and a volunteer for the Provisional Irish Republican Army (IRA), plant
Avtar "Kuku" Kohli was an Indian film director, producer screenwriter and editor. He gave Ajay Devgan his first break in the 1991 film Phool Aur Kaante.
Mary Margaret Morgan, known professionally as Jaye P. Morgan, was an American singer, actress and game show panelist.
Stella Mae Parton is an American country singer and songwriter widely known for a series of country singles that charted during the mid-to-late-1970s, her biggest hit being "I Want to Hold You in My Dreams Tonight" in 1975. She is the younger sister of the lat
John Edward "Jed" York is an American businessman who is the principal owner and chief executive officer of the San Francisco 49ers of the National Football League, as well as co-owner of Premier League club Leeds United and Scottish Premiership club Rangers.
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
2026–27 UEFA Conference League
The 2026–27 UEFA Conference League is the sixth season of the UEFA Conference League, Europe's tertiary club football tournament organised by UEFA.
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.
Harald V was King of Norway from 17 January 1991 until his death in 2026.
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
Rachel Ann Parton George is an American singer and actress and the youngest sibling of entertainer Dolly Parton.
Kenneth Donald Ray Rogers was an American country music singer-songwriter. Rogers was particularly popular with country audiences, but also charted more than 120 hit singles across various genres, topping the country and pop album charts for more than 200 indi
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Reveal of Vision Transformers Robustness against Adversarial Attacks
The major part of the vanilla vision transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. For better performance, ViT needs large-scale training data. To overcome this data hunger limitation, many ViT-based networks, or hybrid-ViT, have been proposed to include local context during the training. The robustness of ViTs and its variants against adversarial a
Federated Adversarial Training with Transformers
Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully crafted samples to yield false classification. Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studi
Using representation balancing to learn conditional-average dose responses from clustered data
Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such a response typically needs to be estimated from observational data, which introduces several challenges. That is why the machine learning (ML) community has proposed several tailored CADR estima
Recurrent Reinforcement Learning with Memoroids
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We discover that the recurrent update of these models resembles a monoid, leading us to
A Dynamic Likelihood Approach to Filtering for Advection-Diffusion Dynamics
A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likelihood (DLF) approach to filtering. The DLF was developed specifically for hyperbolic problems -waves-, and in this paper, it is extended via a split step formulation, to handle advection-diffusion problems. In the dynamic likelihood approach, observations and their stati
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariates, interventions, doses, and outcomes. In recent years, the machine learning community has shown great interest in developing tailored CADR estimators that target specific challenges. Their performance is typically evaluated against other
CollaFuse: Collaborative Diffusion Models
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data availability, computational requirements, and privacy. Traditional approaches to address these shortcomings, like federated learning, often impose significant computational
The BS-meter: Detecting Politics and Labour through ChatGPT's Language
What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, we analyse the distinctive character of language generated by ChatGPT, in relation to questions raised by natural language processing pioneer, and student of Wittgenstein, Margaret Masterman. Following frequent complaints that LLM-based chatbots produce "bullshit," in th
Designing Cellular Manufacturing Systems in the Presence of Alternative Process Plans
In the design of cellular manufacturing systems (CMS), numerous technological and managerial decisions must be made at both the design and operational stages. The first step in designing a CMS involves grouping parts and machines. In this paper, four integer programming formulations are presented for grouping parts and machines in a CMS at both the design and operational levels for a generalised grouping problem, whe
Optimal control and sequential decision making are widely used in many complex tasks. Optimal control over a sequence of natural images is a first step towards understanding the role of vision in control. Here, we formalize this problem as a reinforcement learning task, and derive general conditions under which an image includes enough information to implement an optimal policy. Reinforcement learning is shown to pro
Guided Data Generation for Understanding Model Behavior
We propose a method for generating distributions over the input space as an inspection tool for understanding trained models. Our framework poses questions of the form ``which inputs would make a trained model exhibit a specified behavior?'' and encodes each question through a guidance function. The generated data provide insights into how the models behave. To showcase our framework, we pose queries such as
Investigating Memory in Model-Free RL with POPGym Arcade
How should we analyze memory in deep RL? We introduce tools for analyzing policies under partial observability and revealing how agents use memory to make decisions. To utilize these tools, we present POPGym Arcade, a collection of Atari-inspired, hardware-accelerated environments sharing a single observation and action space. Each environment provides fully and partially observable variants, enabling counterfactual
MODIS: Multi-Omics Data Integration for Small and unpaired datasets
An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: multi-omics data are most often unpaired (requiring diagonal integration), partially labeled with information about biological conditions, and in some situations such as rare diseases, only very small datasets are available. We present MODIS, a semi supervised framework desi
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
Dense retrieval is a crucial task in Information Retrieval (IR), serving as the basis for downstream tasks such as re-ranking and augmenting generation. Recently, large language models (LLMs) have demonstrated impressive semantic understanding capabilities, making them attractive to researchers focusing on dense retrieval. While LLMs, as decoder-style generative models, excel in language generation, they often fall s
pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
Motivated by the growing demand for reduced-precision arithmetic in computational science, we exploit lower-precision emulation in Python{--}widely regarded as the dominant programming language for numerical analysis and machine learning. Low-precision paradigms have revolutionized deep learning by enabling more efficient computation and reduced memory footprint while maintaining model fidelity. To better enable nume
In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems, for which we have access to exact information of constraints and noisy function values of the objective. We propose a Bregman linearized augmented Lagrangian method that utilizes stochastic zeroth-order gradient estimators combined with a variance reduction technique. We analyze its oracle complexity, in terms of the total nu
Plain Transformers Can be Powerful Graph Learners
Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers (GTs) have strayed far from plain Transformers, exhibiting major architectural differences either by integrating message-passing or incorporating sophist
With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within health fact-checking, where effective communication is critical for correcting misconceptions and building trust. Although recent studies have explored LLMs for fact-checking and health communication, differences between L
Temporally-Grounded Language Generation: Towards Real-Time Vision-Language Models
Vision-language models (VLMs) have shown remarkable progress in offline tasks such as image captioning and video question answering. However, real-time interactive environments impose new demands on VLMs, requiring them to generate utterances that are not only semantically accurate but also temporally precise. We identify two core capabilities necessary for such settings---\textit{perceptual updating} and \textit{con
PiT: Progressive Diffusion Transformer
Diffusion Transformers (DiTs) achieve remarkable performance within image generation via the transformer architecture. Conventionally, DiTs are constructed by stacking serial isotropic global modeling transformers, which face significant quadratic computational cost. However, through empirical analysis, we find that DiTs do not rely as heavily on global information as previously believed. In fact, most layers exhibit
Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied across jurisdictions. The advent of Large Language Models (LLMs) offers unprecedented po
HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement
Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their adoption remains limited due to the labor-intensive nature of manual proof construction. Recent advances in large language models (LLMs) have opened new opportunities for automated theorem proving. Two main paradigms have emerged: stepwise tactic-based generation and whole-proo
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthcare where direct interaction with the environment is costly or unsafe. Existing OPE methods are ineffective for high-dimensional, long-horizon problems, due to exponential blow-ups in variance from importance weighting or compounding errors
MObyGaze: a film dataset of multimodal objectification densely annotated by experts
Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perpetuate on screen. In this article, we consider the high-level construct of objectification and introduce a new AI task to the ML community: characterize and quantify complex multimodal (visual, speech, audio) temporal patterns producing objectification in films. Building
From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology behind various large reasoning models such as DeepSeek-R1. In complex domains like mathematical reasoning, rule-based verifiers have been widely adopted in previous works to train strong reasoning models. However, the reliability of these verifiers and their impact on the RL train
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 | 10% (100) | 5.5% (55) | 75.45% (83) |
| llms.txt (apex domain) | 8.1% (81) | 4.5% (45) | 59.09% (65) |
| llms.txt (docs subdomain) | 3.1% (31) | 0.9% (9) | 43.64% (48) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 7.27% (8) |
| .well-known/agents.txt | 0.2% (2) | 0.2% (2) | 0% (0) |
| ai.txt | 0.1% (1) | 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.