Record 02112025 · 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
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
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.
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
Andrew Albert Christian Edward Mountbatten-Windsor, formerly Prince Andrew, Duke of York, is the third child and second son of Queen Elizabeth II and Prince Philip, Duke of Edinburgh, and a younger brother of King Charles III. Andrew was born second in the lin
The Day of the Dead is a holiday traditionally celebrated on November 1 and 2, though other days, such as October 31 or November 6, may be included depending on the locality. The multi-day holiday involves family and friends gathering to pay respects and remem
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
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
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
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.
Halloween, also known as All Hallows' Eve, or All Saints' Eve, is a celebration observed in many countries on 31 October, the eve of the Western Christian feast of All Hallows' Day. It is at the beginning of the observance of Allhallowtide, the time in the Chr
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
Virginia Lee Roberts Giuffre was an American and Australian advocate for survivors of sex trafficking and one of the most prominent accusers of Jeffrey Epstein. Giuffre provided detailed allegations to media outlets about Epstein and Ghislaine Maxwell. She all
Lewis Cope is an English actor and dancer. After beginning his career as a child actor appearing in a West End production of Billy Elliot the Musical, he competed in the Sky One dance series Got to Dance, finishing as a runner-up on the fourth series in 2013.
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
1xBet is an online gambling company founded in 2007 and licensed by Curaçao eGaming License. 1xBet is one of the largest online casinos in the world. According to Forbes, its turnover exceeded $2 billion in 2020. The company sponsors major professional footbal
Weapons is a 2025 American supernatural mystery horror film directed, written, produced, and co-scored by Zach Cregger. It stars an ensemble cast including Josh Brolin, Julia Garner, Alden Ehrenreich, Austin Abrams, Cary Christopher, Toby Huss, Benedict Wong,
Heidi Klum is a German and American model, television host, actress, producer, and businesswoman. She appeared on the cover of the Sports Illustrated Swimsuit Issue in 1998 and was the first German model to become a Victoria's Secret Angel.
Usha Bala Vance is an American lawyer and second lady of the United States since 2025, being the wife of JD Vance, the 50th vice president of the United States. She is the first Indian-American second lady.
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
All Saints' Day is a Christian holy day celebrated in honour of all the saints of the Church, whether they are known or unknown.
Baahubali: The Epic is a 2025 Indian Telugu-language epic action film directed by S. S. Rajamouli, who co-wrote the script with V. Vijayendra Prasad. Produced by Shobu Yarlagadda and Prasad Devineni under Arka Media Works, the film stars Prabhas in dual roles
Lokah Chapter 1: Chandra is a 2025 Indian Malayalam-language fantasy superhero film written and directed by Dominic Arun and produced by Dulquer Salmaan for Wayfarer Films. It stars Kalyani Priyadarshan and Naslen, with Sandy, Chandu Salim Kumar and Arun Kuria
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
James David Vance is an American politician, author, and venture capitalist serving as the 50th vice president of the United States. A member of the Republican Party, he represented the state of Ohio in the United States Senate from 2023 to 2025.
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
Markus Lynn "Mookie" Betts is an American professional baseball outfielder, shortstop, and second baseman for the Los Angeles Dodgers of Major League Baseball (MLB). He debuted in MLB for the Boston Red Sox. Betts is an eight-time All-Star, seven-time Silver S
Saturday Night's Main Event is a series of American professional wrestling television specials produced by WWE. The series originally broadcast from 1985 to 1992, by NBC until 1991 then briefly by Fox. The specials were briefly revived on NBC from 2006 to 2008
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
Tchéky Karyo was a Turkish-born French actor and musician. Beginning his career as an actor on stage in classical and contemporary plays, he later worked as a character actor in films in the 1980s. He acted in numerous films by Hollywood and French directors,
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
This report extends the Spectral Neuro-Symbolic Reasoning (Spectral NSR) framework by introducing three semantically grounded enhancements: (1) transformer-based node merging using contextual embeddings (e.g., Sentence-BERT, SimCSE) to reduce redundancy, (2) sentence-level entailment validation with pretrained NLI classifiers (e.g., RoBERTa, DeBERTa) to improve edge quality, and (3) alignment with external knowledge
Continual Learning, Not Training: Online Adaptation For Agents
Continual Learning (CL) methods have traditionally focused on mitigating catastrophic forgetting through gradient-based retraining, an approach ill-suited for deployed agents that must adapt in real time. We introduce our Adaptive Teaching and Learning System (ATLAS), a dual-agent architecture that decouples reasoning (Teacher) from execution (Student) and incorporates a persistent learning memory that stores distill
The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance In
GeoToken: Hierarchical Geolocalization of Images via Next Token Prediction
Image geolocalization, the task of determining an image's geographic origin, poses significant challenges, largely due to visual similarities across disparate locations and the large search space. To address these issues, we propose a hierarchical sequence prediction approach inspired by how humans narrow down locations from broad regions to specific addresses. Analogously, our model predicts geographic tokens hierar
Deep one-gate per layer networks with skip connections are universal classifiers
This paper shows how a multilayer perceptron with two hidden layers, which has been designed to classify two classes of data points, can easily be transformed into a deep neural network with one-gate layers and skip connections.
Empirical Characterization of Temporal Constraint Processing in LLMs
When deploying LLMs in agentic architectures requiring real-time decisions under temporal constraints, we assume they reliably determine whether action windows remain open or have closed. This assumption is untested. We characterize temporal constraint processing across eight production-scale models (2.8-8B parameters) using deadline detection tasks, revealing systematic deployment risks: bimodal performance distribu
The long-held assumption that backpropagation (BP) is essential for state-of-the-art performance is challenged by this work. We present rigorous, hardware-validated evidence that the Mono-Forward (MF) algorithm, a backpropagation-free method, consistently surpasses an optimally tuned BP baseline in classification accuracy on its native Multi-Layer Perceptron (MLP) architectures. This superior generalization is achiev
Efficient Test-Time Retrieval Augmented Generation
Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but la
Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports poses a persistent challenge. Existing natural language processing (NLP) and machine learning (ML) strategies often depend on large annotated datasets, limiting their scalability and adaptability. In this study, we introduce two Knowledge Elicitation methods designed to ov
HAFixAgent: History-Aware Program Repair Agent
Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic
The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold
Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete memorization of the training data. Previous research has linked this delayed generalization to representation learning driven by weight decay, but the precise underlying dynamics remain elusive. In this paper, we argue that post-memorization learning can be understood through the
On the Emergence of Induction Heads for In-Context Learning
Transformers have become the dominant architecture for natural language processing. Part of their success is owed to a remarkable capability known as in-context learning (ICL): they can acquire and apply novel associations solely from their input context, without any updates to their weights. In this work, we study the emergence of induction heads, a previously identified mechanism in two-layer transformers that is p
Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfer
We analyze subliminal transfer in Transformer models, where a teacher embeds hidden traits that can be linearly decoded by a student without degrading main-task performance. Prior work often attributes transferability to global representational similarity, typically quantified with Centered Kernel Alignment (CKA). Using synthetic corpora with disentangled public and private labels, we distill students under matched a
Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR
Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (RLVR) pipelines filter out ``easy'' problems for training efficiency, leaving the model to train primarily on harder problems that require longer reasoning chains. This skews the output length distribution upward, resulting in a \textbf{mode
OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmo
AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS)
Recurrent exacerbations remain a common yet preventable outcome for many children with asthma. Machine learning (ML) algorithms using electronic medical records (EMR) could allow accurate identification of children at risk for exacerbations and facilitate referral for preventative comprehensive care to avoid this morbidity. We developed ML algorithms to predict repeat severe exacerbations (i.e. asthma-related emergen
Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies
The determination of sample size in qualitative research has traditionally relied on the subjective and often ambiguous principle of data saturation, which can lead to inconsistencies and threaten methodological rigor. This study introduces a new, systematic model based on machine learning (ML) to make this process more objective. Utilizing a dataset derived from five fundamental qualitative research approaches - nam
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often struggles to generalize effectively to unfamiliar or intricate tool-use scenarios. Recently, reinforcement learning (RL) paradigm can endow LLMs
Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be provided via visual features in generated images, but is difficult for human users to understand. Explainability in natural language is a better choice, but the existing a
Aligning LLM agents with human learning and adjustment behavior: a dual agent approach
Effective modeling of how human travelers learn and adjust their travel behavior from interacting with transportation systems is critical for system assessment and planning. However, this task is also difficult due to the complex cognition and decision-making involved in such behavior. Recent research has begun to leverage Large Language Model (LLM) agents for this task. Building on this, we introduce a novel dual-ag
ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL
Large Language Models (LLMs) have demonstrated remarkable progress in translating natural language to SQL, but a significant semantic gap persists between their general knowledge and domain-specific semantics of databases. Historical translation logs constitute a rich source of this missing in-domain knowledge, where SQL queries inherently encapsulate real-world usage patterns of database schema. Existing methods pri
Keys in the Weights: Transformer Authentication Using Model-Bound Latent Representations
We introduce Model-Bound Latent Exchange (MoBLE), a decoder-binding property in Transformer autoencoders formalized as Zero-Shot Decoder Non-Transferability (ZSDN). In identity tasks using iso-architectural models trained on identical data but differing in seeds, self-decoding achieves more than 0.91 exact match and 0.98 token accuracy, while zero-shot cross-decoding collapses to chance without exact matches. This se
Using Synthetic Data to estimate the True Error is theoretically and practically doable
Accurately evaluating model performance is crucial for deploying machine learning systems in real-world applications. Traditional methods often require a sufficiently large labeled test set to ensure a reliable evaluation. However, in many contexts, a large labeled dataset is costly and labor-intensive. Therefore, we sometimes have to do evaluation by a few labeled samples, which is theoretically challenging. Recent
The extent to which large language models (LLMs) can perform culturally grounded reasoning across non-English languages remains underexplored. This paper examines the reasoning and self-assessment abilities of LLMs across seven major Indian languages-Bengali, Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We introduce a multilingual riddle dataset combining traditional riddles with context-reconstructed vari
The Hidden Power of Normalization Layers in Neural Networks: Exponential Capacity Control
Normalization layers are critical components of modern AI systems, such as ChatGPT, Gemini, DeepSeek, etc. Empirically, they are known to stabilize training dynamics and improve generalization ability. However, the underlying theoretical mechanism by which normalization layers contribute to both optimization and generalization remains largely unexplained, especially when using many normalization layers in a deep neur
Notable events recorded on this day and month across all years.