Record 13112025 · captured 2026-08-25
The world looked up Cleto Escobedo III. 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.
Cleto Valentine Escobedo III was an American musician and bandleader. He led Cleto and the Cletones, the house band for Jimmy Kimmel Live!, appearing on the show from its inception in 2003 until his death in 2025. Escobedo began his career touring with Paula A
Dharmendra was an Indian actor, producer and politician, primarily known for his work in Hindi films. He is regarded as one of the greatest and most commercially successful actors in the history of Indian cinema. Known as the "He-man", he was popular for his h
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
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
John Bouvier Kennedy Schlossberg is an American social media personality, political commentator and writer. He is a member of the Kennedy family and the Bouvier family.
Dancing with the Stars (American TV series) season 34
Season thirty-four of Dancing with the Stars premiered on ABC and Disney+ on September 16, 2025, and concluded on December 2, 2025. This season, marking the twentieth anniversary of the series, was the third to air live on both networks simultaneously and the
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
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
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.
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
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
2025 Bihar Legislative Assembly election
Legislative Assembly elections were held in Bihar from 30 April and 7 May 2025, to elect the 243 members of the Bihar Legislative Assembly. The votes were counted and the results were declared on 16 May 2025.
Chester Alan Arthur was the 21st president of the United States, serving from 1881 to 1885. A Republican from New York, he served as the 20th vice president under President James A. Garfield in 1881, assuming the presidency after Garfield's assassination. Arth
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
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
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
Charles Julius Guiteau was an American office seeker who assassinated the 20th President of the United States, James A. Garfield, in 1881. A failed lawyer suffering from mental illness, Guiteau delusionally believed he had played a major role in Garfield's ele
Richard Walter Burton was a Welsh actor.
Predator: Badlands is a 2025 American science fiction action film directed by Dan Trachtenberg and written by Patrick Aison from a story by Trachtenberg and Aison. It is the seventh installment in the Predator franchise and set after the events of The Predator
The Super Mario Galaxy Movie is a 2026 American animated adventure comedy film based on Nintendo's Mario video game franchise. Directed by Aaron Horvath and Michael Jelenic and written by Matthew Fogel, it is the sequel to The Super Mario Bros. Movie (2023). C
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.
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
Xin Zhui, also known as Lady Dai or the Marchioness of Dai, was a Chinese noblewoman. She was the wife of Li Cang (利蒼), the Marquis of Dai (軑) and chancellor of the Changsha Kingdom, during the Western Han dynasty. Her tomb, containing her well-preserved remai
SS Edmund Fitzgerald was an American Great Lakes freighter that sank in Lake Superior during a storm on November 10, 1975, with the loss of the entire crew of 29 men. When launched on June 7, 1958, she was the largest ship on North America's Great Lakes and re
Jacob Nathaniel Elordi is an Australian actor. His accolades include a Critics' Choice Award and three AACTA Awards, in addition to nominations for an Academy Award, three British Academy Film Awards and two Golden Globe Awards.
Sally Kirkland Jr. was an American actress and producer. A one-time member of Andy Warhol's The Factory, she was a part of 1960s New York avant-garde theater. She appeared in more than 250 film and television productions during a 60-year career. Kirkland was t
Benjamin Safdie is an American filmmaker and actor, most known for his film collaborations with his elder brother, Josh, in Heaven Knows What (2014), Good Time (2017), which he also starred in, and Uncut Gems (2019).
Frankenstein; or, The Modern Prometheus is an 1818 Gothic novel written by English author Mary Shelley. Frankenstein tells the story of Victor Frankenstein, a young scientist who creates a sapient creature from different body parts in an unorthodox scientific
Sydney Bernice Sweeney is an American actress. She gained early recognition for her roles in Everything Sucks!, The Handmaid's Tale, and Sharp Objects in 2018. She received wider acclaim for her performances in the drama series Euphoria (2019–2026) and the fir
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Convolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct high-accuracy classification models from scarce data for specific tasks. In such scenarios, fine-tuning the pre-trained CNN is difficult due to data scarcity, necessitating the use of fixed weights. However, when the weights are kept fixed, many filters that do not contribute to
HPCAgentTester: A Multi-Agent LLM Approach for Enhanced HPC Unit Test Generation
Unit testing in High-Performance Computing (HPC) is critical but challenged by parallelism, complex algorithms, and diverse hardware. Traditional methods often fail to address non-deterministic behavior and synchronization issues in HPC applications. This paper introduces HPCAgentTester, a novel multi-agent Large Language Model (LLM) framework designed to automate and enhance unit test generation for HPC software uti
Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for
Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI framework that reconstructs pre-crash scenarios and infers vehicle behaviors from fragmented collision data. We propose a two-phase collaborative framework with reconstruction and reasoning stages. The system processes 277 rear-end lead ve
Digital Twin (DT) technologies are transforming manufacturing by enabling real-time prediction, monitoring, and control of complex processes. Yet, applying DT to deformation-based metal forming remains challenging because of the strongly coupled spatial-temporal behavior and the nonlinear relationship between toolpath and material response. For instance, sheet-metal forming by the English wheel, a highly flexible but
Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs
Task arithmetic is a powerful technique for transferring skills between Large Language Models (LLMs), but it often suffers from negative interference when models have diverged during training. We address this limitation by first aligning the models' parameter spaces, leveraging the inherent permutation, rotation, and scaling symmetries of Transformer architectures. We adapt parameter space alignment for modern Groupe
STAMP: Spatial-Temporal Adapter with Multi-Head Pooling
Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop foundation models specific to electroencephalography (EEG) data, which records brain electrical activity as time series. However, no comparative analysis of EEG-specific foundation models (EEGFMs) versus general TSFMs has been performed on EE
Building on advancements in Large Language Models (LLMs), we can tackle complex analytical and mathematical reasoning tasks requiring nuanced contextual understanding. A prime example of such complex tasks is modelling resource allocation optimization in networks, which extends beyond translating natural language inputs into mathematical equations or Linear Programming (LP), Integer Linear Programming (ILP), and Mixe
Can AI Models be Jailbroken to Phish Elderly Victims? An End-to-End Evaluation
We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deploying those messages against real targets, to successfully compromising elderly victims. We systematically evaluated safety guardrails across six frontier LLMs spanning four attack categories, revealing critical failures where several models
Reinforcing Stereotypes of Anger: Emotion AI on African American Vernacular English
Automated emotion detection is widely used in applications ranging from well-being monitoring to high-stakes domains like mental health and hiring. However, models often rely on annotations that reflect dominant cultural norms, limiting model ability to recognize emotional expression in dialects often excluded from training data distributions, such as African American Vernacular English (AAVE). This study examines em
Optimal Welfare in Noncooperative Network Formation under Attack
Communication networks are essential for our economy and our everyday lives. This makes them lucrative targets for attacks. Today, we see an ongoing battle between criminals that try to disrupt our key communication networks and security professionals that try to mitigate these attacks. However, today's networks, like the Internet or peer-to-peer networks among smart devices, are not controlled by a single authority,
Many reinforcement learning algorithms, particularly those that rely on return estimates for policy improvement, can suffer from poor sample efficiency and training instability due to high-variance return estimates. In this paper we leverage new results from off-policy evaluation; it has recently been shown that well-designed behaviour policies can be used to collect off-policy data for provably lower variance return
HyperComplEx: Adaptive Multi-Space Knowledge Graph Embeddings
Knowledge graphs have emerged as fundamental structures for representing complex relational data across scientific and enterprise domains. However, existing embedding methods face critical limitations when modeling diverse relationship types at scale: Euclidean models struggle with hierarchies, vector space models cannot capture asymmetry, and hyperbolic models fail on symmetric relations. We propose HyperComplEx, a
Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled framework for such tasks, yet their performance is highly sensitive to the choice of control path constructed from discrete observations. Existing methods commonly employ fixed interpolation schemes, which impose simplistic geometric assumptio
Protein Structure Tokenization via Geometric Byte Pair Encoding
Protein structure is central to biological function, and enabling multimodal protein models requires joint reasoning over sequence, structure, and function. A key barrier is the lack of principled protein structure tokenizers (PSTs): existing approaches fix token size or rely on continuous vector codebooks, limiting interpretability, multi-scale control, and transfer across architectures. We introduce GeoBPE, a geome
The Map of Misbelief: Tracing Intrinsic and Extrinsic Hallucinations Through Attention Patterns
Large Language Models (LLMs) are increasingly deployed in safety-critical domains, yet remain susceptible to hallucinations. While prior works have proposed confidence representation methods for hallucination detection, most of these approaches rely on computationally expensive sampling strategies and often disregard the distinction between hallucination types. In this work, we introduce a principled evaluation frame
SCALEX: Scalable Concept and Latent Exploration for Diffusion Models
Image generation models frequently encode social biases, including stereotypes tied to gender, race, and profession. Existing methods for analyzing these biases in diffusion models either focus narrowly on predefined categories or depend on manual interpretation of latent directions. These constraints limit scalability and hinder the discovery of subtle or unanticipated patterns. We introduce SCALEX, a framework for
DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment
Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions. In this paper, we propose DeepDefense, a novel defense framework that applies Gradient-Feature Alignment (GFA) regularization across multiple layers to suppress adversarial vulnerability. By aligning input gradients with internal feature representations, DeepDefen
Bridging the Skills Gap: A Course Model for Modern Generative AI Education
Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across
Operational safety at mission-critical work sites is a top priority given the complex and hazardous nature of daily tasks. This paper presents the Human-Agent Risk Navigation and Event Safety System (HARNESS), a modular AI framework designed to forecast hazardous events and analyze operational risks in U.S. Department of Energy (DOE) environments. HARNESS integrates Large Language Models (LLMs) with structured work d
Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning approaches such as CNNs and Vision Transformers (ViTs) have advanced this field, they often struggle with complex or high-resolution blur and computational demands. We propose a new dual-domain architecture that unifies Vision Transformers with a frequency-domain FFT-ReLU
Discounted Cuts: A Stackelberg Approach to Network Disruption
We study a Stackelberg variant of the classical Most Vital Links problem, modeled as a one-round adversarial game between an attacker and a defender. The attacker strategically removes up to $k$ edges from a flow network to maximally disrupt flow between a source $s$ and a sink $t$, after which the defender optimally reroutes the remaining flow. To capture this attacker--defender interaction, we introduce a new mathe
Classification of Transient Astronomical Object Light Curves Using LSTM Neural Networks
This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The original fourteen object classes were reorganized into five generalized categories (S-Like, Fast, Long, Periodic, and Non-Periodic) to address class imbalance. After preprocessing
Fast Neural Tangent Kernel Alignment, Norm and Effective Rank via Trace Estimation
The Neural Tangent Kernel (NTK) characterizes how a model's state evolves over Gradient Descent. Computing the full NTK matrix is often infeasible, especially for recurrent architectures. Here, we introduce a matrix-free perspective, using trace estimation to rapidly analyze the empirical, finite-width NTK. This enables fast computation of the NTK's trace, Frobenius norm, effective rank, and alignment. We provide num
From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models
Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view overlooks a fundamental challenge: current LLMs apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend rea
Notable events recorded on this day and month across all years.