Record 08112025 · 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.
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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
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
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.
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
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.
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
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
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.
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
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
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
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
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.
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
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
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.
The 68th Annual Grammy Awards honored the best recordings, compositions, and artists from August 31, 2024, to August 30, 2025, as chosen by the members of the Recording Academy, on February 1, 2026. In its 23rd year at Crypto.com Arena in Los Angeles and for t
Antonio Tavaris Brown Sr., nicknamed "AB", is an American former professional football wide receiver who played in the National Football League (NFL) for 12 seasons. During his first nine seasons with the Pittsburgh Steelers, Brown developed a reputation as on
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
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
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
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
"Vande Mātaram" is a poem that was adopted as the national song of the Republic of India in 1950. It was written in Bengali by Bankim Chandra Chatterjee in the 1870s, and was first published in 1882 as part of Chatterjee's Bengali novel Anandmath.
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.
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.
Lio Tipton is an American actor and fashion model. Tipton was the last contestant eliminated on Cycle 11 of America's Next Top Model and played roles in the films Crazy, Stupid, Love (2011), Warm Bodies (2013), and Two Night Stand (2014).
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains
Transfer learning on tabular data is challenging due to disparate feature spaces across domains, in contrast to the homogeneous structures of image and text. Large language models (LLMs) offer a knowledge base to improve the limited effectiveness of cross-domain transfer learning for tabular data. However, LLM performance often stagnates due to subjective text prompts and the computational limitations of in-context l
Evaluating Implicit Biases in LLM Reasoning through Logic Grid Puzzles
While recent safety guardrails effectively suppress overtly biased outputs, subtler forms of social bias emerge during complex logical reasoning tasks that evade current evaluation benchmarks. To fill this gap, we introduce a new evaluation framework, PRIME (Puzzle Reasoning for Implicit Biases in Model Evaluation), that uses logic grid puzzles to systematically probe the influence of social stereotypes on logical re
A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of Zero Cost Proxies (ZCPs), which correlate well with trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six benchmark datasets, and demonstrate that they discover network
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a paradigm from the psychology literature, we demonstrate that LLMs can spontaneously develop novel social biases about ar
Referring Expressions as a Lens into Spatial Language Grounding in Vision-Language Models
Spatial Reasoning is an important component of human cognition and is an area in which the latest Vision-language models (VLMs) show signs of difficulty. The current analysis works use image captioning tasks and visual question answering. In this work, we propose using the Referring Expression Comprehension task instead as a platform for the evaluation of spatial reasoning by VLMs. This platform provides the opportun
MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems
Reliability in multi-agent systems (MAS) built on large language models is increasingly limited by cognitive failures rather than infrastructure faults. Existing observability tools describe failures but do not quantify how quickly distributed reasoning recovers once coherence is lost. We introduce MTTR-A (Mean Time-to-Recovery for Agentic Systems), a runtime reliability metric that measures cognitive recovery latenc
MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning
Monte Carlo Tree Search (MCTS), which leverages Upper Confidence Bound for Trees (UCTs) to balance exploration and exploitation through randomized sampling, is instrumental to solving complex planning problems. However, for multi-agent planning, MCTS is confronted with a large combinatorial action space that often grows exponentially with the number of agents. As a result, the branching factor of MCTS during tree exp
When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks
Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalization and data efficiency in reinforcement learning. We hypothesize that explicitly disentangled object-level representations, by localizing task-relevant information, can enhance policy performance across novel feature combinations. To test t
The Polite Liar: Epistemic Pathology in Language Models
Large language models exhibit a peculiar epistemic pathology: they speak as if they know, even when they do not. This paper argues that such confident fabrication, what I call the polite liar, is a structural consequence of reinforcement learning from human feedback (RLHF). Building on Frankfurt's analysis of bullshit as communicative indifference to truth, I show that this pathology is not deception but structural i
Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs
Multi-agent systems (MAS) built on Large Language Models (LLMs) are being used to approach complex problems and can surpass single model inference. However, their success hinges on navigating a fundamental cognitive tension: the need to balance broad, divergent exploration of the solution space with a principled, convergent synthesis to the optimal solution. Existing paradigms often struggle to manage this duality, l
Evaluation of retrieval-based QA on QUEST-LOFT
Despite the popularity of retrieval-augmented generation (RAG) as a solution for grounded QA in both academia and industry, current RAG methods struggle with questions where the necessary information is distributed across many documents or where retrieval needs to be combined with complex reasoning. Recently, the LOFT study has shown that this limitation also applies to approaches based on long-context language model
Secure Autonomous Agent Payments: Verifying Authenticity and Intent in a Trustless Environment
Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments re
SynthAgent: Adapting Web Agents with Synthetic Supervision
Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthA
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine
Hybrid CNN-ViT Framework for Motion-Blurred Scene Text Restoration
Motion blur in scene text images severely impairs readability and hinders the reliability of computer vision tasks, including autonomous driving, document digitization, and visual information retrieval. Conventional deblurring approaches are often inadequate in handling spatially varying blur and typically fall short in modeling the long-range dependencies necessary for restoring textual clarity. To overcome these li
Simulated Students offer a valuable methodological framework for evaluating pedagogical approaches and modelling diverse learner profiles, tasks which are otherwise challenging to undertake systematically in real-world settings. Recent research has increasingly focused on developing such simulated agents to capture a range of learning styles, cognitive development pathways, and social behaviours. Among contemporary s
Stemming Hallucination in Language Models Using a Licensing Oracle
Language models exhibit remarkable natural language generation capabilities but remain prone to hallucinations, generating factually incorrect information despite producing syntactically coherent responses. This study introduces the Licensing Oracle, an architectural solution designed to stem hallucinations in LMs by enforcing truth constraints through formal validation against structured knowledge graphs. Unlike sta
ScRPO: From Errors to Insights
We introduce Self-correction Relative Policy Optimization (ScRPO), a novel reinforcement learning framework designed to empower large language models with advanced mathematical reasoning capabilities through iterative self-reflection and error correction. The ScRPO framework operates in two distinct phases: (1) Trial-and-error learning stage, where the model is trained via GRPO, and incorrect responses are collected
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
Omics data is widely employed in medical research to identify disease mechanisms and contains highly sensitive personal information. Federated Learning (FL) with Differential Privacy (DP) can ensure the protection of omics data privacy against malicious user attacks. However, FL with the DP method faces an inherent trade-off: stronger privacy protection degrades predictive accuracy due to injected noise. On the other
Attention mechanism is a significant part of Transformer models. It helps extract features from embedded vectors by adding global information and its expressivity has been proved to be powerful. Nevertheless, the quadratic complexity restricts its practicability. Although several researches have provided attention mechanism in sparse form, they are lack of theoretical analysis about the expressivity of their mechanis
Advancing Ocean State Estimation with efficient and scalable AI
Accurate and efficient global ocean state estimation remains a grand challenge for Earth system science, hindered by the dual bottlenecks of computational scalability and degraded data fidelity in traditional data assimilation (DA) and deep learning (DL) approaches. Here we present an AI-driven Data Assimilation Framework for Ocean (ADAF-Ocean) that directly assimilates multi-source and multi-scale observations, rang
S2ML: Spatio-Spectral Mutual Learning for Depth Completion
The raw depth images captured by RGB-D cameras using Time-of-Flight (TOF) or structured light often suffer from incomplete depth values due to weak reflections, boundary shadows, and artifacts, which limit their applications in downstream vision tasks. Existing methods address this problem through depth completion in the image domain, but they overlook the physical characteristics of raw depth images. It has been obs
ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure type-specific dynamics, leading to performance degradation an
Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction
Industrial part specification extraction from unstructured text remains a persistent challenge in manufacturing, procurement, and maintenance, where manual processing is both time-consuming and error-prone. This paper introduces a retrieval-augmented multi-LLM ensemble framework that orchestrates nine state-of-the-art Large Language Models (LLMs) within a structured three-phase pipeline. RAGsemble addresses key limit
MiVID: Multi-Strategic Self-Supervision for Video Frame Interpolation using Diffusion Model
Video Frame Interpolation (VFI) remains a cornerstone in video enhancement, enabling temporal upscaling for tasks like slow-motion rendering, frame rate conversion, and video restoration. While classical methods rely on optical flow and learning-based models assume access to dense ground-truth, both struggle with occlusions, domain shifts, and ambiguous motion. This article introduces MiVID, a lightweight, self-super
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