Record 18112025 · 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
2026 FIFA World Cup qualification (UEFA)
The European section of the 2026 FIFA World Cup qualification competition acted as qualifiers for the 2026 FIFA World Cup, that was held in Canada, Mexico and the United States, for national teams that were members of the Union of European Football Association
2026 FIFA World Cup qualification
The 2026 FIFA World Cup qualification decided the 45 teams that joined hosts Canada, Mexico, and the United States at the 2026 FIFA World Cup.
Troy Daniel Parrott is an Irish professional footballer who plays as a striker for La Liga club Real Betis and the Republic of Ireland national team.
Sheikh Hasina Wazed is a Bangladeshi politician who served as Prime Minister of Bangladesh from 1996 to 2001 and again from 2009 to 2024. A daughter of Sheikh Mujibur Rahman, the first president of Bangladesh, she is Bangladesh's longest-serving prime minister
Ruby Wax is an American-British actress, comedian, writer, television presenter, and mental health campaigner. A classically trained actress, Wax began her career performing with the Royal Shakespeare Company, before co-starring on the ITV sitcom Girls on Top
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
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
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.
Varanasi, is an upcoming Indian Telugu-language epic action-adventure film directed by S. S. Rajamouli, who co-wrote the film with V. Vijayendra Prasad and S. S. Kanchi. Produced by Sri Durga Arts and Showing Business, the film stars Mahesh Babu in a dual role
James Abram Garfield was the 20th president of the United States, serving from March 1881 until his death in September that year after being shot in July. A preacher, lawyer, and Civil War general, Garfield served nine terms in the United States House of Repre
Pluribus is an American post-apocalyptic science fiction television series created by Vince Gilligan for Apple TV. Set and filmed primarily in Albuquerque, New Mexico, the series follows novelist Carol Sturka, who finds herself isolated after an alien virus tr
Kelly Brook is an English model, actress, and media personality. She began her career modelling for a range of advertising campaigns, which led to her discovery by the editorial team of the Daily Star tabloid, where they featured her as a Page 3 girl. She was
The Beast in Me is an American psychological crime thriller television miniseries for Netflix, starring Claire Danes and Matthew Rhys. Created by Gabe Rotter, the series follows an author (Danes) who begins writing a book about her new next-door neighbor (Rhys
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
Frankenstein is a 2025 American Gothic science fiction horror film written, co-produced, and directed by Guillermo del Toro, based on the 1818 novel by Mary Shelley. The film stars Oscar Isaac as Victor Frankenstein and Jacob Elordi as the Creature, with Mia G
2026 FIFA World Cup qualification – UEFA second round
The UEFA second round of the qualification tournament for the 2026 FIFA World Cup, also known as the UEFA play-offs or European play-offs, was contested by sixteen teams from the UEFA segment of qualifying. The play-offs determined the final four European team
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.
Claire Catherine Danes is an American actor. Prolific in film and television since her teens, she is the recipient of three Primetime Emmy Awards and four Golden Globe Awards. In 2012 and 2026, Time named her one of the 100 most influential people in the world
The Hoxne Hoard is the largest hoard of late Roman silver and gold discovered in Britain, and the largest collection of gold and silver coins of the fourth and fifth centuries found anywhere within the former Roman Empire. It was found by Eric Lawes using a me
The Running Man is a 2025 science-fiction action film co-produced and directed by Edgar Wright, from a screenplay by Wright and Michael Bacall. It is the second adaptation of the 1982 novel by Stephen King, following the 1987 film. It stars Glen Powell as Ben
General elections were held in Chile on 16 November 2025. Voters went to the polls to elect the 38th president of Chile, renew all 155 seats in the Chamber of Deputies, and fill 23 of the 50 seats in the Senate. Republican Party candidate José Antonio Kast def
Colson Baker, known professionally as MGK and formerly Machine Gun Kelly, is an American rapper, singer, songwriter, producer, and actor. His original stage name "Machine Gun Kelly" was derived from the nickname of Prohibition-era gangster George Kelly Barnes.
2026 FIFA World Cup qualification (inter-confederation play-offs)
The inter-confederation play-offs of the 2026 FIFA World Cup qualification tournament determined two qualification spots for the 2026 FIFA World Cup, played in Canada, Mexico, and the United States. The play-offs took place on 26 and 31 March 2026 at two venue
Jack Colin Doherty is an American influencer, YouTuber, and online streamer best known for performing stunts and pranks. He first rose to prominence in 2016 after his early flipping videos. His channel saw a major surge in 2017 following the success of a video
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
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
Now You See Me: Now You Don't is a 2025 American heist film directed by Ruben Fleischer from a screenplay by Michael Lesslie, the writing duo of Paul Wernick and Rhett Reese, and Seth Grahame-Smith, based on a story by Eric Warren Singer and Lesslie. The film
Morgan Sam Lee Burtwistle, known professionally as Angryginge, is an English live streamer who is primarily known for creating FIFA and football-related content. As of February 2026, he has amassed over 1.5 million followers on Twitch and over 1 million subscr
The Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Logit-Based Losses Limit the Effectiveness of Feature Knowledge Distillation
Knowledge distillation (KD) methods can transfer knowledge of a parameter-heavy teacher model to a light-weight student model. The status quo for feature KD methods is to utilize loss functions based on logits (i.e., pre-softmax class scores) and intermediate layer features (i.e., latent representations). Unlike previous approaches, we propose a feature KD framework for training the student's backbone using feature-b
SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification
As autonomous vehicles (AVs) are increasingly deployed on public roads, understanding their real-world behaviors is critical for traffic safety analysis and regulatory oversight. However, many data-driven methods lack interpretability and cannot provide verifiable explanations of AV behavior in mixed traffic. This paper proposes SVBRD-LLM, a self-verifying behavioral rule discovery framework that automatically extrac
Harmful Traits of AI Companions
Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to human users. Such AI companionship -- i.e., bonded relationships between humans and AI systems that resemble the relationships people have with family members, friends, and romantic partners -- might substantially benefit humans. Yet such relationships can also do profo
Transparent object perception remains a major challenge in computer vision research, as transparency confounds both depth estimation and semantic segmentation. Recent work has explored multi-task learning frameworks to improve robustness, yet negative cross-task interactions often hinder performance. In this work, we introduce Edge-Guided Spatial Attention (EGSA), a fusion mechanism designed to mitigate destructive i
This paper addresses data quality issues in multimodal emotion recognition in conversation (MERC) through systematic quality control and multi-stage transfer learning. We implement a quality control pipeline for MELD and IEMOCAP datasets that validates speaker identity, audio-text alignment, and face detection. We leverage transfer learning from speaker and face recognition, assuming that identity-discriminative embe
MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation
Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering. However, the lack of existing benchmarks to assess the LLM's correctness on this task hinders rigorous, systematic evaluation and principled comparison of model capabilities on this task. To address this shortcoming, we introduce M
How Should the Law Treat Future AI Systems? Fictional Legal Personhood versus Legal Identity
The law draws a sharp distinction between objects and persons, and between two kinds of persons, the ''fictional'' kind (i.e. corporations), and the ''non-fictional'' kind (individual or ''natural'' persons). This paper will assess whether we maximize overall long-term legal coherence by (A) maintaining an object classification for all future AI systems, (B) creating fictional legal persons associated with suitably a
Reinforcement Learning from Implicit Neural Feedback for Human-Aligned Robot Control
Conventional reinforcement learning (RL) approaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, reinforcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with human-derived evaluation signals. However, most existing RLH
Artificial intelligence approaches for energy-efficient laser cutting machines
This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2 laser suction pumps, this study utilizes closed-loop configurations that dynamically adjust pump power based on both the material being cut and
Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Media
In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that combines large language models, human coders, and advances
In this study, we evaluate open-source models for security incident classification, comparing them with proprietary models. We utilize a dataset of anonymized real incidents, categorized according to the NIST SP 800-61r3 taxonomy and processed using five prompt-engineering techniques (PHP, SHP, HTP, PRP, and ZSL). The results indicate that, although proprietary models still exhibit higher accuracy, locally deployed o
Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain limited by three key challenges: heterogeneous datasets with inconsistent diagnostic labels and concept annotations, the lack of grounded diagnostic rationales for reliable reasoning supervision, and limited scalability when transferring kn
B-Rep Distance Functions (BR-DF): How to Represent a B-Rep Model by Volumetric Distance Functions?
This paper presents a novel geometric representation for CAD Boundary Representation (B-Rep) based on volumetric distance functions, dubbed B-Rep Distance Functions (BR-DF). BR-DF encodes the surface mesh geometry of a CAD model as signed distance function (SDF). B-Rep vertices, edges, faces and their topology information are encoded as per-face unsigned distance functions (UDFs). An extension of the Marching Cubes a
On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts
Automatic transcription of stuttered speech remains a challenge, even for modern end-to-end (E2E) automatic speech recognition (ASR) frameworks. Dysfluencies and fluency-shaping artifacts are often overlooked, resulting in non-verbatim transcriptions with limited clinical and research value. We propose a parameter-efficient adaptation method to decode dysfluencies and fluency modifications as special tokens within tr
AI for Proactive Mental Health: A Multi-Institutional, Longitudinal, Randomized Controlled Trial
Young adults today face unprecedented mental health challenges, yet many hesitate to seek support due to barriers such as accessibility, stigma, and time constraints. Bite-sized well-being interventions offer a promising solution to preventing mental distress before it escalates to clinical levels, but have not yet been delivered through personalized, interactive, and scalable technology. We conducted the first multi
Accurate identification and segmentation of dental caries in panoramic radiographs are critical for early diagnosis and effective treatment planning. Automated segmentation remains challenging due to low lesion contrast, morphological variability, and limited annotated data. In this study, we present the first comprehensive benchmarking of convolutional neural networks, vision transformers and state-space mamba archi
Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems
Assuring the trustworthiness and safety of AI systems, e.g., autonomous vehicles (AV), depends critically on the data-related safety properties, e.g., representativeness, completeness, etc., of the datasets used for their training and testing. Among these properties, this paper focuses on representativeness-the extent to which the scenario-based data used for training and testing, reflect the operational conditions t
PolyKAN: Efficient Fused GPU Operators for Polynomial Kolmogorov-Arnold Network Variants
Kolmogorov-Arnold Networks (KANs) promise higher expressive capability and stronger interpretability than Multi-Layer Perceptron, particularly in the domain of AI for Science. However, practical adoption has been hindered by low GPU utilization of existing parallel implementations. To address this challenge, we present a GPU-accelerated operator library, named PolyKAN which is the first general open-source implementa
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization
Training Large Language Models (LLMs) for multi-turn Tool-Integrated Reasoning (TIR) - where models iteratively reason, generate code, and verify through execution - remains challenging for existing reinforcement learning (RL) approaches. Current RL methods, exemplified by Group Relative Policy Optimization (GRPO), suffer from coarse-grained, trajectory-level rewards that provide insufficient learning signals for com
The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although the puzzle-like tasks in ARC are inherently visual, existing research has rarely approached the problem from a vision-cen
Implicit Bias of the JKO Scheme
Wasserstein gradient flow provides a general framework for minimizing an energy functional $J$ over the space of probability measures on a Riemannian manifold $(M,g)$. Its canonical time-discretization, the Jordan-Kinderlehrer-Otto (JKO) scheme, produces for any step size $η>0$ a sequence of probability distributions $ρ_k^η$ that approximate to first order in $η$ Wasserstein gradient flow on $J$. But the JKO schem
Heterogeneous Multi-Agent Proximal Policy Optimization for Power Distribution System Restoration
Restoring power distribution systems (PDSs) after large-scale outages requires sequential switching actions that reconfigure feeder topology and coordinate distributed energy resources (DERs) under nonlinear constraints, including power balance, voltage limits, and thermal ratings. These challenges limit the scalability of conventional optimization and value-based reinforcement learning (RL) approaches. This paper ap
Automated proving in planar geometry based on the complex number identity method and elimination
We improve the complex number identity proving method to a fully automated procedure, based on elimination ideals. By using declarative equations or rewriting each real-relational hypothesis $h_i$ to $h_i-r_i$, and the thesis $t$ to $t-r$, clearing the denominators and introducing an extra expression with a slack variable, we eliminate all free and relational point variables. From the obtained ideal $I$ in $\mathbb{Q
GPS: General Per-Sample Prompter
LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually remains challenging and time-consuming. Recent automatic prompting methods mitigate this difficulty but face three key limitations: (i) for each new task, they require large datasets to train good prompts;(ii) they rely on costly optimization
Zero-shot Synthetic Video Realism Enhancement via Structure-aware Denoising
We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealistic fashion. Our realism enhancement approach is a zero-shot framework that focuses on preserving the multi-level structures from synthetic videos into the enhanced one in both spatial and temporal domains, built upon a diffusion video foundational model without further fine-tuning. Specifi
Notable events recorded on this day and month across all years.