Record 07112025 · captured 2026-08-25
The world looked up Zohran Mamdani. 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.
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
Marshawn Kneeland was an American professional football defensive end for the Dallas Cowboys of the National Football League (NFL). He played two seasons with the Cowboys until his death in 2025. Kneeland played college football for the Western Michigan Bronco
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.
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.
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
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
Pauline Angela Collins was a British actress who first rose to fame portraying Sarah Moffat in Upstairs, Downstairs (1971–1973) and its spin-off Thomas & Sarah (1979). In 1992, she published her autobiography, Letter to Louise.
UPS Airlines Flight 2976 was a scheduled domestic cargo flight in the United States from Louisville Muhammad Ali International Airport in Louisville, Kentucky, to Honolulu, Hawaii. On November 4, 2025, the McDonnell Douglas MD-11 operating the flight suffered
John Alderton is an English retired actor. He is best known for his roles in Upstairs, Downstairs, Thomas & Sarah, Wodehouse Playhouse, Little Miss, Please Sir!, No - Honestly and Fireman Sam. Alderton often starred alongside his second wife, Pauline Collins.
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
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.
Nancy Patricia Pelosi is an American politician who was the 52nd speaker of the United States House of Representatives, serving from 2007 to 2011 and again from 2019 to 2023. A member of the Democratic Party, she was the first female elected speaker and the fi
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
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
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
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
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.
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
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
The Gospel of Judas is a Gnostic religious text that consists of conversations between Jesus and his disciples, especially Judas Iscariot. The only copy of it known to exist is a Coptic language text that is part of the Codex Tchacos, which has been radiocarbo
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
Jaafar Jeremiah Jackson is an American actor and singer. A member of the Jackson family, he released his debut single "Got Me Singing" in 2019 but gained popularity for portraying his uncle Michael Jackson in the record-breaking biographical film Michael (2026
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
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
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
The mayor of New York City, officially mayor of the City of New York, is head of the executive branch of the government of New York City and the chief executive of New York City. The mayor's office administers all city services, public property, police and fir
The Celebrity Traitors is a spin-off of the British version of the reality television series The Traitors. It was first broadcast on BBC One on 8 October 2025. Claudia Winkleman presented the series.
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.
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
The McDonnell Douglas MD-11 is an American trijet wide-body airliner which was manufactured by McDonnell Douglas and later by Boeing. The MD-11 is the largest trijet ever built.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Youth increasingly turn to large language models (LLMs) for mental well-being support, yet current personalization in LLMs can overlook the heterogeneous lived experiences shaping their needs. We conducted a participatory study with youth, parents, and youth care workers (N=38), using co-created youth personas as scaffolds, to elicit community perspectives on how LLMs can facilitate more meaningful personalization to
Anchors in the Machine: Behavioral and Attributional Evidence of Anchoring Bias in LLMs
Large language models (LLMs) are increasingly examined as both behavioral subjects and decision systems, yet it remains unclear whether observed cognitive biases reflect surface imitation or deeper probability shifts. Anchoring bias, a classic human judgment bias, offers a critical test case. While prior work shows LLMs exhibit anchoring, most evidence relies on surface-level outputs, leaving internal mechanisms and
Toward Better Generalization in Few-Shot Learning through the Meta-Component Combination
In few-shot learning, classifiers are expected to generalize to unseen classes given only a small number of instances of each new class. One of the popular solutions to few-shot learning is metric-based meta-learning. However, it highly depends on the deep metric learned on seen classes, which may overfit to seen classes and fail to generalize well on unseen classes. To improve the generalization, we explore the subs
Language Generation: Complexity Barriers and Implications for Learning
Kleinberg and Mullainathan showed that language generation in the limit is always possible at the level of computability: given enough positive examples, a learner can eventually generate data indistinguishable from a target language. However, such existence results do not address feasibility. We study the sample complexity of language generation in the limit for several canonical classes of formal languages. Our res
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization
Long Chain-of-Thought (CoT) traces can improve reasoning accuracy, but repeatedly generating them is costly for smaller or latency-constrained language models. This paper studies a practical alternative: produce a rich rationale once with a capable \emph{thinking} model, compress it, and reuse the compressed trace as context for a cheaper \emph{answering} model. We introduce CoT-X, an adaptive framework for cross-mod
Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting large language models (LLMs) by decomposing token activations into combinations of human-understandable features. While SAEs provide crucial insights into LLM explanations, their practical adoption faces a fundamental challenge: better interpretability demands that SAEs' hidden layers have high dimensionality to satisfy sparsity constraints,
Compressing Chemistry Reveals Functional Groups
We introduce the first formal large-scale assessment of the utility of traditional chemical functional groups as used in chemical explanations. Our assessment employs a fundamental principle from computational learning theory: a good explanation of data should also compress the data. We introduce an unsupervised learning algorithm based on the Minimum Message Length (MML) principle that searches for substructures tha
OckBench: Measuring the Efficiency of LLM Reasoning
Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current benchmarks emphasize accuracy and output quality, neglecting a critical dimension: efficiency of token usage. The token efficiency is highly variable in practical. Models solving the same problem with similar accuracy can exhibit up to a \textbf{5.0$\times$} difference in token leng
Academic advising is critical to student success in higher education, yet high student-to-advisor ratios limit advisors' capacity to provide timely support, particularly during peak periods. Recent advances in Large Language Models (LLMs) present opportunities to enhance the advising process. We present AdvisingWise, a multi-agent system that automates time-consuming tasks, such as information retrieval and response
Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale
Despite rapid progress, multimodal reasoning still lacks a systematic approach to synthesize large-scale vision-centric datasets beyond visual math. We introduce a framework able to synthesize vision-centric problems spanning diverse levels of complexity, and the resulting dataset with over 1M high-quality problems including: reasoning traces, preference data, and instruction prompts supporting SFT, offline and onlin
TabDistill: Distilling Transformers into Neural Nets for Few-Shot Tabular Classification
Transformer-based models have shown promising performance on tabular data compared to their classical counterparts such as neural networks and Gradient Boosted Decision Trees (GBDTs) in scenarios with limited training data. They utilize their pre-trained knowledge to adapt to new domains, achieving commendable performance with only a few training examples, also called the few-shot regime. However, the performance gai
Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging
Graphical user interface (GUI) grounding, the process of mapping human instructions to GUI actions, serves as a fundamental basis to autonomous GUI agents. While existing grounding models achieve promising performance to simulate the mouse click action on various click-based benchmarks, another essential mode of mouse interaction, namely dragging, remains largely underexplored. Yet, dragging the mouse to select and m
This paper offers a concise, 60-year synthesis of human-AI collaboration, from Licklider's ``man-computer symbiosis" (AI as colleague) and Engelbart's ``augmenting human intellect" (AI as tool) to contemporary poles: Human-Centered AI's ``supertool" and Symbiotic Intelligence's mutual-adaptation model. We formalize the mechanism for effective teaming as a causal chain: Explainable AI (XAI) -> co-adaptation -> s
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended generation tasks. We propose Base-Aligned Model Collaboration (BACo), an inference-time token-level model collaboration framework that dynamically combines a base LLM with its aligned counterpart to optimize diversity and quality. Using unce
Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose T
DGTN: Graph-Enhanced Transformer with Diffusive Attention Gating Mechanism for Enzyme DDG Prediction
Predicting the effect of amino acid mutations on enzyme thermodynamic stability (DDG) is fundamental to protein engineering and drug design. While recent deep learning approaches have shown promise, they often process sequence and structure information independently, failing to capture the intricate coupling between local structural geometry and global sequential patterns. We present DGTN (Diffused Graph-Transformer
On Flow Matching KL Divergence
We derive a deterministic, non-asymptotic upper bound on the Kullback-Leibler (KL) divergence of the flow-matching distribution approximation. In particular, if the $L_2$ flow-matching loss is bounded by $ε^2 > 0$, then the KL divergence between the true data distribution and the estimated distribution is bounded by $A_1 ε+ A_2 ε^2$. Here, the constants $A_1$ and $A_2$ depend only on the regularities of the data a
AI Literacy Assessment Revisited: A Task-Oriented Approach Aligned with Real-world Occupations
As artificial intelligence (AI) systems become ubiquitous in professional contexts, there is an urgent need to equip workers, often with backgrounds outside of STEM, with the skills to use these tools effectively as well as responsibly, that is, to be AI literate. However, prevailing definitions and therefore assessments of AI literacy often emphasize foundational technical knowledge, such as programming, mathematics
Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulati
SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workflows. Existing benchmarks often focus on algorithmic problems or Python-centric bug fixing, leaving critical dimensions of software engineering underexplored. To address these gaps, we introduce SWE-Compass1, a comprehensive benchmark that
Self-adaptive weighting and sampling for physics-informed neural networks
Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting method to enhance the performance of physics-informed neural networks (PINNs). The adaptive sampling compo
APP: Accelerated Path Patching with Task-Specific Pruning
Circuit discovery is a key step in many mechanistic interpretability pipelines. Current methods, such as Path Patching, are computationally expensive and have limited in-depth circuit analysis for smaller models. In this study, we propose Accelerated Path Patching (APP), a hybrid approach leveraging our novel contrastive attention head pruning method to drastically reduce the search space of circuit discovery methods
As artificial intelligence (AI) increasingly shapes decision-making across domains, there is a growing need to support AI literacy among learners beyond computer science. However, many current approaches rely on programming-heavy tools or abstract lecture-based content, limiting accessibility for non-STEM audiences. This paper presents findings from a study of AI User, a modular, web-based curriculum that teaches cor
ProDER: A Continual Learning Approach for Fault Prediction in Evolving Smart Grids
As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical. However, existing AI-based fault prediction models struggle to ensure reliability in evolving environments where they are required to adapt to new fault types and operational zones. In this paper, we propose a continual learning (CL) framework in the smart gri
Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments
Robust loop closure detection is a critical component of Simultaneous Localization and Mapping (SLAM) algorithms in GNSS-denied environments, such as in the context of planetary exploration. In these settings, visual place recognition often fails due to aliasing and weak textures, while LiDAR-based methods suffer from sparsity and ambiguity. This paper presents MPRF, a multimodal pipeline that leverages transformer-b
Notable events recorded on this day and month across all years.