Record 05112025 · captured 2026-08-25
The world looked up Dick Cheney. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
Richard Bruce Cheney was an American politician and businessman who served as the 46th vice president of the United States under President George W. Bush from 2001 to 2009. Considered the main architect of the Iraq War, Cheney has been called the most powerful
Zohran Kwame Mamdani is an American politician who has served since 2026 as the 112th mayor of New York City. A member of the Democratic Party and the Democratic Socialists of America, he represented the 36th district in the New York State Assembly from 2021 t
2025 New York City mayoral election
An election for the mayor of New York City was held on November 4, 2025. Democratic state assemblyman Zohran Mamdani won the election with 50.78% of the vote, defeating Republican activist Curtis Sliwa and independent former Democratic governor Andrew Cuomo. T
Diane Ladd was an American actress. With a career spanning over 70 years, she appeared in over 200 films and television shows, receiving three Academy Award nominations for her roles in Alice Doesn't Live Here Anymore (1974), Wild at Heart (1990) and Rambling
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Jonathan Stuart Bailey is an English actor known for his dramatic, comedic, and musical roles on stage and screen. His accolades include a Laurence Olivier Award and a Critics' Choice Television Award as well as a nomination for a Primetime Emmy Award and four
Curtis Anthony Sliwa is an American politician, activist and radio talk show host at 710 WOR Radio in NYC. He is the founder and chief executive officer of the Guardian Angels, a nonprofit crime-prevention organization headquartered in New York City. Sliwa was
Andrew Mark Cuomo is an American lawyer and politician who served as the 56th governor of New York from 2011 until his resignation in 2021. A member of the Democratic Party and son of former governor Mario Cuomo, he served in numerous state and national office
Rama Sawaf Duwaji is an American animator, illustrator, and ceramist. As the wife of Mayor Zohran Mamdani, she has been the first lady of New York City since January 2026.
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
Margaret D. H. Keane was an American artist known for her paintings of subjects with big eyes. She mainly painted women, children, or animals in oil or mixed media. The work achieved commercial success through inexpensive reproductions on prints, plates, and c
Fuck is a profanity in the English language. It often refers to the act of sexual intercourse, but it is most commonly used as an intensifier or to convey disdain. While its origin is obscure, it is usually considered to be first attested to around 1475. In mo
Edward Theodore Gein, also known as the Butcher of Plainfield and the Plainfield Ghoul, was an American murderer and body snatcher. His crimes, committed around his hometown of Plainfield, Wisconsin, gathered widespread notoriety in 1957 after authorities disc
Laura Elizabeth Dern is an American actress. She is the recipient of numerous accolades, including an Academy Award, an Actor Award, a BAFTA Award, a Primetime Emmy Award, and five Golden Globes.
Jacoby JaJuan Brissett is an American professional football quarterback for the Arizona Cardinals of the National Football League (NFL). Following a stint with the Florida Gators, he played college football for the NC State Wolfpack and was selected by the New
6-7 was an Internet meme, slang term, and gesture that became popular in 2025 on TikTok and Instagram Reels. It has no fixed meaning.
Mira Nair is an Indian American filmmaker. She has received two prizes from the Cannes Film Festival and four from the Venice Film Festival, as well as nominations for an Academy Award, two BAFTA Awards, a Golden Globe, and two César Awards.
Bruce MacLeish Dern is an American actor. He has received several accolades, including the Cannes Film Festival Award for Best Actor for Nebraska (2013), which also earned him a nomination for the Academy Award for Best Actor, and won the Silver Bear for Best
Government shutdowns in the United States
In the United States, a government shutdown, officially known as a lapse in appropriations, occurs when funding legislation required to finance the federal government is not enacted before the next fiscal year begins. During a shutdown, the federal government
Mahmood Mamdani is a Ugandan anthropologist, academic, and political commentator. He is the Herbert Lehman Professor of Government and a professor of anthropology, political science, and African studies at Columbia University. He also served as the chancellor
Elizabeth Lynne Cheney is an American attorney and former politician who was the U.S. representative for Wyoming's at-large congressional district from 2017 to 2023, and served as chair of the House Republican Conference from 2019 to 2021. A member of the Repu
Elections were held in the United States on November 4, 2025. The off-year election included gubernatorial and state legislative elections in a few states, as well as numerous mayoral races and a variety of other local offices on the ballot. Special elections
A company is a legal entity representing an association of legal persons with a shared objective, such as generating profit or benefiting society. Depending on the jurisdiction, companies can take on various forms, including voluntary associations, nonprofit o
Sir David Robert Joseph Beckham is an English former professional footballer, the president and co-owner of Inter Miami CF and co-owner of Salford City F.C.. Primarily a right midfielder and known for his range of passing, crossing ability and set-piece taking
On February 11, 2006, then-United States vice president Dick Cheney shot Harry Whittington, a then-78-year-old Texas attorney, with a 28-gauge Perazzi shotgun while participating in a quail hunt on a ranch in Riviera, Texas. Both Cheney and Whittington called
Nicholas Joseph Fuentes is an American far-right political commentator, live streamer, and influencer. He hosts the livestreamed show America First, where he has advanced white nationalism and white supremacy, Christian nationalism, the incel subculture, misog
It: Welcome to Derry is an American supernatural horror television series based on Stephen King's 1986 novel It. Serving as a prequel to the films It (2017) and It Chapter Two (2019), the series was developed by Andy Muschietti, Barbara Muschietti and Jason Fu
Mary Claire Cheney is the youngest of two daughters of Dick Cheney, the 46th vice president of the United States and 17th United States secretary of defense, and Lynne Cheney. She is involved with a number of political action committees. She married her wife,
George Walker Bush is an American former politician, businessman, and former United States Air Force officer who served as the 43rd president of the United States from 2001 to 2009. A member of the Republican Party, he served as the governor of Texas from 1995
Jamie Lee Melham is an Australian jockey. In 2020/21 she became the first jockey to ride 100 winners in a Melbourne Metropolitan racing season. In 2020 and 2021 she was the leading female jockey in the world. She is the second female jockey to win the Melbourn
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Evolutionary Optimization Trumps Adam Optimization on Embedding Space Exploration
Deep diffusion models have revolutionized image generation by producing high-quality outputs. However, achieving specific objectives with these models often requires costly adaptations such as fine-tuning, which can be resource-intensive and time-consuming. An alternative approach is inference-time control, which involves optimizing the prompt embeddings to guide the generation process without altering the model weig
Unknown anomaly detection in medical imaging remains a fundamental challenge due to the scarcity of labeled anomalies and the high cost of expert supervision. We introduce an unsupervised, oracle-free framework that incrementally expands a trusted set of normal samples without any anomaly labels. Starting from a small, verified seed of normal images, our method alternates between lightweight adapter updates and uncer
SnappyMeal: Design and Longitudinal Evaluation of a Multimodal AI Food Logging Application
Food logging, both self-directed and prescribed, plays a critical role in uncovering correlations between diet, medical, fitness, and health outcomes. Through conversations with nutritional experts and individuals who practice dietary tracking, we find current logging methods, such as handwritten and app-based journaling, are inflexible and result in low adherence and potentially inaccurate nutritional summaries. The
Secure Code Generation at Scale with Reflexion
Large language models (LLMs) are now widely used to draft and refactor code, but code that works is not necessarily secure. We evaluate secure code generation using the Instruct Prime, which eliminated compliance-required prompts and cue contamination, and evaluate five instruction-tuned code LLMs using a zero-shot baseline and a three-round reflexion prompting approach. Security is measured using the Insecure Code D
Small, imbalanced datasets and poor input image quality can lead to high false predictions rates with deep learning models. This paper introduces Class-Based Image Composition, an approach that allows us to reformulate training inputs through a fusion of multiple images of the same class into combined visual composites, named Composite Input Images (CoImg). That enhances the intra-class variance and improves the valu
Large language models (LLMs) have achieved impressive results in high-resource languages like English, yet their effectiveness in low-resource and morphologically rich languages remains underexplored. In this paper, we present a comprehensive evaluation of seven cutting-edge LLMs -- including GPT-4o, GPT-4, Claude~3.5~Sonnet, LLaMA~3.1, Mistral~Large~2, LLaMA-2~Chat~13B, and Mistral~7B~Instruct -- on a new cross-ling
Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures
Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided procedures, such as spine instrumentation, with sparse inputs. We examine the feasibility, opportunities and challenges for imitation policy learning in bi-plane-guided cannula insertion. We develop an in silico sandbox for scalable, automated
KnowThyself: An Agentic Assistant for LLM Interpretability
We develop KnowThyself, an agentic assistant that advances large language model (LLM) interpretability. Existing tools provide useful insights but remain fragmented and code-intensive. KnowThyself consolidates these capabilities into a chat-based interface, where users can upload models, pose natural language questions, and obtain interactive visualizations with guided explanations. At its core, an orchestrator LLM f
OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
Recent advances in large language models (LLMs) have significantly accelerated progress in code translation, enabling more accurate and efficient transformation across programming languages. While originally developed for natural language processing, LLMs have shown strong capabilities in modeling programming language syntax and semantics, outperforming traditional rule-based systems in both accuracy and flexibility.
Levers of Power in the Field of AI
This paper examines how decision makers in academia, government, business, and civil society navigate questions of power in implementations of artificial intelligence. The study explores how individuals experience and exercise levers of power, which are presented as social mechanisms that shape institutional responses to technological change. The study reports on the responses of personalized questionnaires designed
Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets
Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 detection from Chest X-rays (CXRs), in particular, has been shown to fail to generalize to out-of-distribution (OOD) data from new clinical sources not covered in the training set. This occurs because models learn to exploit shortcuts - source-specific artifacts that do not t
To See or To Read: User Behavior Reasoning in Multimodal LLMs
Multimodal Large Language Models (MLLMs) are reshaping how modern agentic systems reason over sequential user-behavior data. However, whether textual or image representations of user behavior data are more effective for maximizing MLLM performance remains underexplored. We present \texttt{BehaviorLens}, a systematic benchmarking framework for assessing modality trade-offs in user-behavior reasoning across six MLLMs b
CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment
Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-to-fine framework CORE for accurate nuclei-level registration across diverse multimodal whole-slide image (WSI) datasets. The coarse registration stage leverages prompt-based tissue mask extraction to effectively filter out artefacts and non
How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis
Tokenization is fundamental in assembly code analysis, impacting intrinsic characteristics like vocabulary size, semantic coverage, and extrinsic performance in downstream tasks. Despite its significance, tokenization in the context of assembly code remains an underexplored area. This study aims to address this gap by evaluating the intrinsic properties of Natural Language Processing (NLP) tokenization models and par
PLLuM: A Family of Polish Large Language Models
Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for other languages. We present PLLuM (Polish Large Language Model), the largest open-source family of foundation models tailored specifically for the Polish language. Developed by a consortium of major Polish research institutions, PLLuM address
This study explores whether large language models (LLMs) can simulate valid student responses for educational measurement. Using GPT -4o, 2000 virtual student personas were generated. Each persona completed the Academic Motivation Scale (AMS). Factor analyses(EFA and CFA) and clustering showed GPT -4o reproduced the AMS structure and distinct motivational subgroups.
Optimizing Reasoning Efficiency through Prompt Difficulty Prediction
Reasoning language models perform well on complex tasks but are costly to deploy due to their size and long reasoning traces. We propose a routing approach that assigns each problem to the smallest model likely to solve it, reducing compute without sacrificing accuracy. Using intermediate representations from s1.1-32B, we train lightweight predictors of problem difficulty or model correctness to guide routing across
EvalCards: A Framework for Standardized Evaluation Reporting
Evaluation has long been a central concern in NLP, and transparent reporting practices are more critical than ever in today's landscape of rapidly released open-access models. Drawing on a survey of recent work on evaluation and documentation, we identify three persistent shortcomings in current reporting practices: reproducibility, accessibility, and governance. We argue that existing standardization efforts remain
Expert Evaluation of LLM World Models: A High-$T_c$ Superconductivity Case Study
Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the le
Learning Interestingness in Automated Mathematical Theory Formation
We take two key steps in automating the open-ended discovery of new mathematical theories, a grand challenge in artificial intelligence. First, we introduce $\emph{FERMAT}$, a reinforcement learning (RL) environment that models concept discovery and theorem-proving using a set of symbolic actions, opening up a range of RL problems relevant to theory discovery. Second, we explore a specific problem through $\emph{FERM
Scaling Agent Learning via Experience Synthesis
While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity, all of which obstruct the collection of scalable experience data. To address these challenges, we introduce DreamGym, the first unified framework designed to
AI researchers have long focused on poker-like games as a testbed for environments characterized by multi-player dynamics, imperfect information, and reasoning under uncertainty. While recent breakthroughs have matched elite human play at no-limit Texas hold'em, the multi-player dynamics are subdued: most hands converge quickly with only two players engaged through multiple rounds of bidding. In this paper, we presen
Climbing the label tree: Hierarchy-preserving contrastive learning for medical imaging
Medical image labels are often organized by taxonomies (e.g., organ - tissue - subtype), yet standard self-supervised learning (SSL) ignores this structure. We present a hierarchy-preserving contrastive framework that makes the label tree a first-class training signal and an evaluation target. Our approach introduces two plug-in objectives: Hierarchy-Weighted Contrastive (HWC), which scales positive/negative pair str
Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask
Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for each reference expression, enabling us to trace how understandi
The Illusion of Procedural Reasoning: Measuring Long-Horizon FSM Execution in LLMs
Large language models (LLMs) have achieved remarkable results on tasks framed as reasoning problems, yet their true ability to perform procedural reasoning, executing multi-step, rule-based computations remains unclear. Unlike algorithmic systems, which can deterministically execute long-horizon symbolic procedures, LLMs often degrade under extended reasoning chains, but there is no controlled, interpretable benchmar
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