Record 26012026 · captured 2026-08-25
The world looked up Killing of Alex Pretti. 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.
On January 24, 2026, Alex Jeffrey Pretti, a 37-year-old American intensive care nurse for the United States Department of Veterans Affairs, was shot multiple times and killed by two United States Customs and Border Protection officers in Minneapolis, Minnesota
Alexander J Honnold is an American rock climber best known for his free solo ascents of big wall climbing routes. Honnold rose to worldwide fame in June 2017 when he became the first person to free solo a full route on El Capitan in Yosemite National Park via
Jarrett Ryan Stidham is an American professional football quarterback for the Denver Broncos of the National Football League (NFL). He played college football for the Auburn Tigers following a stint with the Baylor Bears. Stidham was selected by the New Englan
Border 2 is a 2026 Indian Hindi-language epic war film co-written and directed by Anurag Singh. A sequel to J. P. Dutta's 1997 film Border, it was produced by Bhushan Kumar, Krishan Kumar, J. P. Dutta, and Nidhi Dutta under the banners of T-Series Films and J.
UFC 324: Gaethje vs. Pimblett was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on January 24, 2026, at the T-Mobile Arena in Paradise, Nevada, part of the Las Vegas Valley, United States.
Patrick Mark Pimblett is an English professional mixed martial artist. A professional since 2012, Pimblett is a former Cage Warriors Featherweight Champion. He currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC). As of 11
Justin Ray Gaethje is an American professional mixed martial artist. A professional since 2011, he competes in the lightweight division of the Ultimate Fighting Championship (UFC), where he is the current UFC Lightweight Champion. He is the first fighter in UF
Taipei 101, formerly known as the Taipei World Financial Center, is a 508-meter, 101-story skyscraper in Taipei, Taiwan. It is owned by the Taipei Financial Center Corporation. It was officially classified as the world's tallest building from its opening on 31
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
Bats are winged mammals, the only mammals capable of true and sustained flight. Bats are more agile in flight than most birds, using long, spread-out digits covered with a thin membrane or patagium. The smallest bat, and one of the smallest extant mammals, is
Gregory Kent Bovino is a United States Border Patrol officer who served as the commander-at-large of the Border Patrol from October 2025 to January 2026.
Teyana Me Shay Jacqueline Taylor is an American singer, songwriter, actress, dancer, choreographer, and music video director. Her accolades include a Golden Globe Award, two Critics Choice Awards, and an NAACP Image Award, along with nominations for an Academy
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
The Rip is a 2026 American action thriller film written and directed by Joe Carnahan, who developed the story with Michael McGrale. The film stars Matt Damon and Ben Affleck as police officers in the Miami-Dade Police Department narcotics unit. It also stars S
Michael John McCarthy is an American professional football coach who is the head coach for the Pittsburgh Steelers of the National Football League (NFL). Previously, he served as the head coach of the Dallas Cowboys and Green Bay Packers. In 2011, McCarthy led
Elizabeth Ann Gilmour is an American child safety activist and commentator for ABC News. She was put into the national spotlight in 2002 at age 14 when she was abducted from her home in Salt Lake City by Brian David Mitchell. Mitchell and his wife, Wanda Barze
Kyle Howard Rittenhouse is an American man who gained national attention at age 17 for shooting three men in Kenosha, Wisconsin, two fatally, amid protests and riots in response to the police shooting of Jacob Blake in 2020.
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Learner Tien is an American professional tennis player. He has a career-high ATP singles ranking of world No. 12 achieved on August 10, 2026 and doubles ranking of No. 298 achieved on May 25, 2026. Tien has won two ATP Tour singles titles, as well as the 2025
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
Ryan James Wedding is a Canadian former Olympic snowboarder and alleged drug lord. He represented Canada at the 2002 Winter Olympics in the men's parallel giant slalom event. After retiring from snowboarding, he allegedly became an international drug trafficke
On January 7, 2026, Renée Nicole Macklin Good, a 37-year-old American woman, was fatally shot by United States Immigration and Customs Enforcement (ICE) agent Jonathan Ross in Minneapolis, Minnesota, during Operation Metro Surge. Good was in her car stopped si
Mark Kerr is an American former wrestler and mixed martial artist. During his MMA career, he was a two-time UFC Heavyweight Tournament Champion, World Vale Tudo Championship tournament winner, and a PRIDE FC competitor. In collegiate wrestling, Kerr was an NCA
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
The 2026 Royal Rumble, also promoted as Royal Rumble: Riyadh, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. It was the 39th annual Royal Rumble and took place on January 31, 2026, at Riyadh Season
Republic Day is a national holiday in India commemorating the adoption of the Constitution of the Republic of India and the country's transition to a republic which came into effect on 26 January 1950.
Derrick Martell Rose is an American former professional basketball player. He played one year of college basketball for the Memphis Tigers before being drafted first overall by his hometown Chicago Bulls in the 2008 NBA draft. Nicknamed "D-Rose", and sometimes
Kristi Lynn Arnold Noem is an American politician serving as the United States special envoy for the Shield of the Americas since 2026. From 2025 to 2026, she served as the eighth United States secretary of homeland security. A member of the Republican Party,
Sean Daniel O'Malley is an American professional mixed martial artist. He currently competes in the Bantamweight division of the Ultimate Fighting Championship (UFC), where he is a former UFC Bantamweight Champion. As of June 20, 2026, he is #3 in the Meta UFC
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Failures of Contingent Thinking
We present a behavioral definition of an agent's perceived implication that uniquely identifies a subjective state-space representing her view of a decision problem, and which may differ from the modeler's. By examining belief updating within this model, we formalize the recent empirical consensus that reducing uncertainty improves contingent thinking, and propose a novel form of updating corresponding to the
Vertical Semi-Federated Learning for Efficient Online Advertising
Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-world industrial applications, where the learned model retains
Task Aware Dreamer for Task Generalization in Reinforcement Learning
A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. The ability to generalize across tasks is important as it determines an agent's adaptability to real-world scenarios where reward mechanisms might vary. In this work, we first show that training a general worl
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting
When solving NLP tasks with limited labelled data, researchers typically either use a general large language model without further update, or use a small number of labelled samples to tune a specialised smaller model. In this work, we answer an important question -- how many labelled samples are required for the specialised small models to outperform general large models, while taking the performance variance into co
Text classification is a crucial and fundamental task in web content mining. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification,
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks
Type 1 diabetes mellitus (T1D) is characterized by insulin deficiency and blood glucose (BG) control issues. The state-of-the-art solution for continuous BG control is reinforcement learning (RL), where an agent can dynamically adjust exogenous insulin doses in time to maintain BG levels within the target range. However, due to the lack of action guidance, the agent often needs to learn from randomized trials to unde
PanoNormal: Monocular Indoor 360° Surface Normal Estimation
The presence of spherical distortion in equirectangular projection (ERP) images presents a persistent challenge in dense regression tasks such as surface normal estimation. Although it may appear straightforward to repurpose architectures developed for 360° depth estimation, our empirical findings indicate that such models yield suboptimal performance when applied to surface normal prediction. This is largely attribu
Bayesian Joint Additive Factor Models for Multiview Learning
It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and responses. A motivating application arises in the context of precision medicine where multi-omics data are collected to correlate with clinical outcomes. It is of interest to infer dependence within and across views while combining multimodal info
The role of geographical proximity in facilitating inter-regional or inter-organizational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level still needs to be addressed. Using publication data in the field of artificial intelligence from 2001 to 2019, in this work, the effect of geographical proximity o
Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction
Mixture of experts is a prediction aggregation method in machine learning that aggregates the predictions of specialized experts. This method often outperforms Bayesian methods despite the Bayesian having stronger inductive guarantees. We argue that this is due to the greater functional capacity of mixture of experts. We prove that in a limiting case of mixture of experts will have greater capacity than equivalent Ba
In open-set semi-supervised learning (OSSL), we consider unlabeled datasets that may contain unknown classes. Existing OSSL methods often use the softmax confidence for classifying data as in-distribution (ID) or out-of-distribution (OOD). Additionally, many works for OSSL rely on ad-hoc thresholds for ID/OOD classification, without considering the statistics of the problem. We propose a new score for ID/OOD classifi
Provable Differentially Private Computation of the Cross-Attention Mechanism
Cross-attention has emerged as a cornerstone module in modern artificial intelligence, underpinning critical applications such as retrieval-augmented generation (RAG), system prompting, and guided stable diffusion. However, this is a rising concern about securing the privacy of cross-attention, as the underlying key and value matrices frequently encode sensitive data or private user information. In this work, we intr
Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality between variables within the temporal chain, which is crucial for various scientific disciplines. Compared to causal discovery from non-time-series data, causal discovery from time-series data necessitates more serialized samples with a larger amount of observed time steps. To address the
Linguistic traces of stochastic empathy in language models
Differentiating generated and human-written content is increasingly difficult. We examine how an incentive to convey humanness and task characteristics shape this human vs AI race across five studies. In Study 1-2 (n=530 and n=610) humans and a large language model (LLM) wrote relationship advice or relationship descriptions, either with or without instructions to sound human. New participants (n=428 and n=408) judge
Unified Multimodal Interleaved Document Representation for Retrieval
Information Retrieval (IR) methods aim to identify documents relevant to a query, which have been widely applied in various natural language tasks. However, existing approaches typically consider only the textual content within documents, overlooking the fact that documents can contain multiple modalities, including images and tables. Also, they often segment each long document into multiple discrete passages for emb
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models
Text-to-image (T2I) models are increasingly used in impactful real-life applications. As such, there is a growing need to audit these models to ensure that they generate desirable, task-appropriate images. However, systematically inspecting the associations between prompts and generated content in a human-understandable way remains challenging. To address this, we propose Concept2Concept, a framework where we charact
On Fine-Grained I/O Complexity of Attention Backward Passes
Large Language Models (LLMs) exhibit exceptional proficiency in handling extensive context windows in natural language. Nevertheless, the quadratic scaling of attention computation relative to sequence length creates substantial efficiency bottlenecks, necessitating the development of I/O-optimized algorithms. In this work, we conduct a systematic examination of the I/O complexity inherent in attention mechanisms, wi
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries
Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired by real-world
We study the problem of robot navigation in dense and interactive crowds with static constraints such as corridors and furniture. Previous methods fail to consider all types of spatial and temporal interactions among agents and obstacles, leading to unsafe and inefficient robot paths. In this article, we leverage a graph-based representation of crowded and constrained scenarios and propose a structured framework to l
Towards Fast Safe Online Reinforcement Learning via Policy Finetuning
The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods. While offline safe RL addresses this by learning policies from static datasets, the performance therein is usually limited due to reliance on data quality and challenges with out-of-distribution (OOD) actions. Inspired by recent successes in offline-to-on
Modern Hopfield Networks Require Chain-of-Thought to Solve $\mathsf{NC}^1$-Hard Problems
Modern Hopfield Networks (MHNs) have emerged as powerful components in deep learning, serving as effective replacements for pooling layers, LSTMs, and attention mechanisms. While recent advancements have significantly improved their storage capacity and retrieval efficiency, their fundamental theoretical boundaries remain underexplored. In this paper, we rigorously characterize the expressive power of MHNs through th
MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery
Crack detection is a critical task in structural health monitoring, aimed at assessing the structural integrity of bridges, buildings, and roads to prevent potential failures. Vision-based crack detection has become the mainstream approach due to its ease of implementation and effectiveness. Fusing infrared (IR) channels with red, green and blue (RGB) channels can enhance feature representation and thus improve crack
Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random
ViSymRe: Vision Multimodal Symbolic Regression
Extracting interpretable equations from observational datasets to describe complex natural phenomena is one of the core goals of artificial intelligence. This field is known as symbolic regression (SR). In recent years, Transformer-based paradigms have become a new trend in SR, addressing the well-known problem of inefficient search. However, the modal heterogeneity between datasets and equations often hinders the co
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