Record 04112025 · captured 2026-08-25
The world looked up Google Chrome. 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.
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
The ICC Women's Cricket World Cup is the quadrennial international championship of the One Day International format with 50 overs per team. It is organised by the International Cricket Council.
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
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
Harmanpreet Kaur Bhullar is an Indian cricketer who plays as an all-rounder and captains the India women's national team. She is a top order batter and a right-arm off-spin bowler. She captained the Indian team that won the 2025 Women's Cricket World Cup, the
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
Smriti Mandhana is an Indian international cricketer and the vice-captain of the Indian women's national team. She was part of the Indian team that won the 2025 Women's Cricket World Cup, the Women's Asia Cup in 2016 and 2022. She also won a gold medal in the
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.
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
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
Mithali Raj is an Indian former cricketer who captained the national team from 2004 to 2022. She is the highest run-scorer in women's international cricket, and ESPN ranked her as one of the greatest female cricketers of all time. Raj has received several nati
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.
Amol Anil Muzumdar is an Indian cricket coach and former cricketer who previously played for the cricket teams of Mumbai and Assam. He was primarily a right-handed batsman. He held the record for the most runs scored in the Ranji Trophy, India's premier domest
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
Shohei Ohtani is a Japanese professional baseball designated hitter and pitcher for the Los Angeles Dodgers of Major League Baseball (MLB). Nicknamed "Shotime", he has previously played in MLB for the Los Angeles Angels and in Nippon Professional Baseball (NPB
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
India women's national cricket team
The India women's national cricket team represents India in international cricket. It is governed by the Board of Control for Cricket in India (BCCI) and is a full member of the International Cricket Council with Test, One Day International and Twenty20 Intern
Yoshinobu Yamamoto is a Japanese professional baseball pitcher for the Los Angeles Dodgers of Major League Baseball (MLB). He has previously played in Nippon Professional Baseball (NPB) for the Orix Buffaloes, where he became one of the most decorated pitchers
Aileen Carol Wuornos was an American serial killer. Between 1989 and 1990, while engaging in street prostitution along highways in Florida, Wuornos shot, killed, and robbed seven of her male clients. She initially claimed that her victims had either raped or a
Beauty trends among American conservatives
United States conservatives in the 2020s have demonstrated at least two notable beauty trends. The first is Republican makeup, also known as MAGA makeup, MAGA beauty or conservative girl makeup. This term describes the style and application of cosmetics by peo
Bugonia is a 2025 dark comedy film directed by Yorgos Lanthimos and written by Will Tracy. An English-language remake of the 2003 South Korean film Save the Green Planet! by Jang Joon-hwan, the film follows two young men who kidnap a powerful CEO, suspecting t
Jemimah Jessica Rodrigues is an Indian international cricketer who plays for the India women's national team as a middle-order batter. She was part of the squad that won the 2025 Women's Cricket World Cup, the Women's Asia Cup in 2022, the gold medal at the 20
Kantara: A Legend – Chapter 1 is a 2025 Indian Kannada-language epic mythological action drama film co-written and directed by Rishab Shetty, and produced by Vijay Kiragandur and Chaluve Gowda under Hombale Films. The film stars Rishab Shetty in a quadruple ro
A House of Dynamite is a 2025 American political thriller film directed by Kathryn Bigelow and written by Noah Oppenheim. The film dramatizes the perspectives and responses of various U.S. government officials, both civilian and military, after an unknown adve
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 Philip Anthony Hopkins is a Welsh actor. Considered one of Britain's most recognisable and prolific actors, he is known for his performances on the screen and stage. Hopkins has received numerous accolades, including two Academy Awards, four BAFTA Awards,
The Witcher is a fantasy drama television series created by Lauren Schmidt Hissrich for Netflix. It is based on the book series by Polish author Andrzej Sapkowski. Set on a fictional, medieval-inspired landmass known as the Continent, The Witcher explores the
Palash Muchhal is an Indian music composer. He is the brother of Indian singer Palak Muchhal and brother-in-law of Indian singer and composer Mithoon. He began composing music for Bollywood films as a teenager. He began dating the cricketer Smriti Mandhana in
2025 Women's Cricket World Cup
The 2025 ICC Women's Cricket World Cup was the 13th edition of the Women's Cricket World Cup. India hosted the World Cup for the fourth time, after the 1978, 1997 and 2013 editions, with the tournament held from 30 September to 2 November 2025. This was the la
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge
Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable progress, further capabilities are expected in order to achieve more general types of intelligence. A critical distinction in this context is between factual knowledge, which can be evaluated against true or false answers (e.g., "what is t
Reading Between the Lines: The One-Sided Conversation Problem
Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing the missing speaker's turns for real-time use cases, and (2) generating summaries from one-side
No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchmarking study that systematically compares 36 LLMs, including GPT, Gemini, Claude, and Llama, across multiple product categories using a consensus-driven evaluation protocol. Our multi-agent framework aggregates pattern audits and issue codes
Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
Vision-Language Models (VLMs) have shown strong performance in tasks like visual question answering and multimodal text generation, but their effectiveness in scientific domains such as materials science remains limited. While some machine learning methods have addressed specific challenges in this field, there is still a lack of foundation models designed for broad tasks like polymer property prediction using multim
This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images, the proposed approach enables efficient feature extraction through Convolutional Neural Networks (CNNs) while ensuring that sensitive medical data remain local to each device
PublicAgent: Multi-Agent Design Principles From an LLM-Based Open Data Analysis Framework
Open data repositories hold potential for evidence-based decision-making, yet are inaccessible to non-experts lacking expertise in dataset discovery, schema mapping, and statistical analysis. Large language models show promise for individual tasks, but end-to-end analytical workflows expose fundamental limitations: attention dilutes across growing contexts, specialized reasoning patterns interfere, and errors propaga
Adaptive-Sensorless Monitoring of Shipping Containers
Monitoring the internal temperature and humidity of shipping containers is essential to preventing quality degradation during cargo transportation. Sensorless monitoring -- machine learning models that predict the internal conditions of the containers using exogenous factors -- shows promise as an alternative to monitoring using sensors. However, it does not incorporate telemetry information and correct for systemati
Exploratory Analysis of Cyberattack Patterns on E-Commerce Platforms Using Statistical Methods
Cyberattacks on e-commerce platforms have grown in sophistication, threatening consumer trust and operational continuity. This research presents a hybrid analytical framework that integrates statistical modelling and machine learning for detecting and forecasting cyberattack patterns in the e-commerce domain. Using the Verizon Community Data Breach (VCDB) dataset, the study applies Auto ARIMA for temporal forecasting
SLIP: Structural-aware Language-Image Pretraining for Vision-Language Alignment
Vision-Language Pretraining (VLP) has achieved remarkable success across various downstream tasks, but such gains are largely driven by scaling up on training data. Yet, literature methods treat image-text pairs as isolated training examples; this neglects the rich relational structure naturally present in many domains, such as e-commerce product co-purchase graphs and social recommendation networks. Inspired by neur
Large Language Models (LLMs) distinguish themselves by quickly delivering information and providing personalized responses through natural language prompts. However, they also infer user demographics, which can raise ethical concerns about bias and implicit personalization and create an echo chamber effect. This study aims to explore how inferred political views impact the responses of ChatGPT globally, regardless of
Evaluating Control Protocols for Untrusted AI Agents
As AI systems become more capable and widely deployed as agents, ensuring their safe operation becomes critical. AI control offers one approach to mitigating the risk from untrusted AI agents by monitoring their actions and intervening or auditing when necessary. Evaluating the safety of these protocols requires understanding both their effectiveness against current attacks and their robustness to adaptive adversarie
Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications
The design and application of LLM-based personas in AI companionship is a rapidly expanding but fragmented field, spanning from virtual emotional companions and game NPCs to embodied functional robots. This diversity in objectives, modality, and technical stacks creates an urgent need for a unified framework. To address this gap, this paper systematizes the field by proposing a Four-Quadrant Technical Taxonomy for AI
Value of Information-Enhanced Exploration in Bootstrapped DQN
Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $ε$-greedy and Boltzmann exploration methods, often struggle to efficiently balance exploration and exploitation. In this paper, we integrate the notion of (expect
EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation
Event cameras, with their high dynamic range (HDR) and low latency, offer a promising alternative for robust depth estimation in challenging environments. However, many event-based depth estimation approaches are constrained by small-scale annotated datasets, limiting their generalizability to real-world scenarios. To bridge this gap, we introduce EvtSlowTV, a large-scale event camera dataset curated from publicly av
Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics
A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action's effectiveness (habituation), while inactivity may restore it (recovery). These nonstationarities are captured by the Reducing or Gaining Unknown Efficacy (ROGUE) bandit framework, which models real-world settings such as behavioral health
From Narrow to Wide: Autoencoding Transformers for Ultrasound Bandwidth Recovery
Conventional pulse-echo ultrasound suffers when low-cost probes deliver only narrow fractional bandwidths, elongating pulses and erasing high-frequency detail. We address this limitation by learning a data-driven mapping from band-limited to broadband spectrogram of radio-frequency (RF) lines. To this end, a variation of Tiny Vision Transform (ViT) auto-encoder is trained on simulation data using a curriculum-weighte
Zero-shot data citation function classification using transformer-based large language models (LLMs)
Efforts have increased in recent years to identify associations between specific datasets and the scientific literature that incorporates them. Knowing that a given publication cites a given dataset, the next logical step is to explore how or why that data was used. Advances in recent years with pretrained, transformer-based large language models (LLMs) offer potential means for scaling the description of data use ca
Graph Neural Networks (GNNs) based on spectral filters, such as the Adaptive Orthogonal Polynomial Filter (AOPF) class (e.g., LaguerreNet), have shown promise in unifying the solutions for heterophily and over-smoothing. However, these single-filter models suffer from a "compromise" problem, as their single adaptive parameter (e.g., alpha) must learn a suboptimal, averaged response across the entire graph spectrum. I
Data augmentation is widely used in vision to introduce variation and mitigate overfitting, by enabling models to learn invariant properties. However, augmentation only indirectly captures these properties and does not explicitly constrain the learned function to satisfy them beyond the empirical training set. We propose generative hints, a training methodology that directly enforces known functional invariances over
Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything
Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs and require costly fine-tuning with large aligned datasets. Building fully omni-capable models that can integrate text, images, audio, and video remains impractical and lacks robust reasoning support. In this paper, we propose an Agent-Omni framework that coordinates existing foundation models through a
Neurosymbolic Deep Learning Semantics
Artificial Intelligence (AI) is a powerful new language of science as evidenced by recent Nobel Prizes in chemistry and physics that recognized contributions to AI applied to those areas. Yet, this new language lacks semantics, which makes AI's scientific discoveries unsatisfactory at best. With the purpose of uncovering new facts but also improving our understanding of the world, AI-based science requires formalizat
Kosmos: An AI Scientist for Autonomous Discovery
Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that can automate scientific research, but all such agents remain limited in the number of actions they can take before losing coherence, thus limiting the depth of their findings. Here we present Kosmos, an AI scientist that automates data-dri
Optimizing AI Agent Attacks With Synthetic Data
As AI deployments become more complex and high-stakes, it becomes increasingly important to be able to estimate their risk. AI control is one framework for doing so. However, good control evaluations require eliciting strong attack policies. This can be challenging in complex agentic environments where compute constraints leave us data-poor. In this work, we show how to optimize attack policies in SHADE-Arena, a data
Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
Tabular data remain the predominant format for real-world applications. Yet, developing effective neural models for tabular data remains challenging due to heterogeneous feature types and complex interactions occurring at multiple scales. Recent advances in tabular in-context learning (ICL), such as TabPFN and TabICL, have achieved state-of-the-art performance comparable to gradient-boosted trees (GBTs) without task-
Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities
As model context lengths continue to grow, concerns about whether models effectively use the full context length have persisted. While several carefully designed long-context evaluations have recently been released, these evaluations tend to rely on retrieval from one or more sections of the context, which allows nearly all of the context tokens to be disregarded as noise. This represents only one type of task that m
Notable events recorded on this day and month across all years.