Record 06112025 · 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
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
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
Abigail Anne Spanberger is an American politician and former intelligence officer serving since 2026 as the 75th governor of Virginia. A member of the Democratic Party, she served from 2019 to 2025 as the U.S. representative for Virginia's 7th congressional di
Rebecca Michelle "Mikie" Sherrill is an American politician, former naval officer, and former federal prosecutor serving since 2026 as the 57th governor of New Jersey. She is a member of the Democratic Party.
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
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
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
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
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
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
2025 New Jersey gubernatorial election
The 2025 New Jersey gubernatorial election was held on November 4, 2025, to elect the governor of New Jersey. Democratic U.S. representative Mikie Sherrill defeated Republican former nominee Jack Ciattarelli. Sherrill succeeded Democratic incumbent Phil Murphy
2025 Virginia gubernatorial election
The 2025 Virginia gubernatorial election was held on November 4, 2025, to elect the governor of Virginia. Democratic former congresswoman Abigail Spanberger won her first term in office, defeating Republican lieutenant governor Winsome Earle-Sears. Spanberger
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.
Eric Leroy Adams is an American politician and former police officer who served as the 111th mayor of New York City from 2022 to 2025. A member of the Democratic Party, Adams was an officer in the New York City Transit Police and then the New York City Police
Elections were held in the United States on November 4, 2025. The off-year election included gubernatorial and state legislative elections in a few states, as well as numerous mayoral races and a variety of other local offices on the ballot. Special elections
Ghazala Firdous Hashmi is an American politician serving as the 43rd lieutenant governor of Virginia since 2026. A member of the Democratic Party, she previously served as a Virginia state senator for the 15th district from 2020 to 2026. She is the first Asian
2025 California Proposition 50
California Proposition 50, officially known as the Election Rigging Response Act, is an amendment to the constitution of the U.S. state of California, which was passed by voters in a special election ballot on November 4, 2025. At the urging of California gove
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
2021 New York City mayoral election
An election for the mayor of New York City was held on November 2, 2021. Incumbent mayor Bill de Blasio was term-limited and ineligible to run for re-election. Democratic Brooklyn Borough president and former police officer Eric Adams won the election in a lan
Guy Fawkes Night, also known as Guy Fawkes Day, Bonfire Night and Fireworks Night, is an annual commemoration observed on 5 November, primarily in Great Britain, involving bonfires and fireworks displays. Its history begins with the events of 5 November 1605 O
Glenn Allen Youngkin is an American politician and businessman who served as the 74th governor of Virginia from 2022 to 2026. A member of the Republican Party, he spent 25 years at The Carlyle Group, a private equity firm, where he became co-CEO in 2018.
Winsome Earle-Sears is a Jamaican–American politician and businesswoman who served as the 42nd lieutenant governor of Virginia from 2022 to 2026. A member of the Republican Party, she represented the 90th district in the Virginia House of Delegates from 2002 t
Guy Fawkes, also known as Guido Fawkes while fighting for the Spanish, was a member of a group of provincial English Catholics involved in the failed Gunpowder Plot of 1605. He was born and educated in York; his father died when Fawkes was eight years old, aft
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.
Mitchell Epstein is an American photographer and author. His books include Vietnam: A Book of Changes (1997); Family Business (2003), which won the 2004 Kraszna-Krausz Photography Book Award; Recreation: American Photographs 1973–1988 (2005); Mitch Epstein: Wo
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
REFLEX: Reference-Free Evaluation of Log Summarization via Large Language Model Judgment
Evaluating log summarization systems is challenging due to the lack of high-quality reference summaries and the limitations of existing metrics like ROUGE and BLEU, which depend on surface-level lexical overlap. We introduce REFLEX, a reference-free evaluation metric for log summarization based on large language model (LLM) judgment. REFLEX uses LLMs as zero-shot evaluators to assess summary quality along dimensions
DMA: Online RAG Alignment with Human Feedback
Retrieval-augmented generation (RAG) systems often rely on static retrieval, limiting adaptation to evolving intent and content drift. We introduce Dynamic Memory Alignment (DMA), an online learning framework that systematically incorporates multi-granularity human feedback to align ranking in interactive settings. DMA organizes document-, list-, and response-level signals into a coherent learning pipeline: supervise
Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs
Recent studies have revealed that LLMs can exhibit behavioral self-awareness: the ability to accurately describe or predict their own learned behaviors without explicit supervision. This capability raises safety concerns as it may, for example, allow models to better conceal their true abilities during evaluation. We attempt to characterize the minimal conditions under which such self-awareness emerges, and the mecha
Report from Workshop on Dialogue alongside Artificial Intelligence
Educational dialogue -- the collaborative exchange of ideas through talk -- is widely recognized as a catalyst for deeper learning and critical thinking in and across contexts. At the same time, artificial intelligence (AI) has rapidly emerged as a powerful force in education, with the potential to address major challenges, personalize learning, and innovate teaching practices. However, these advances come with signi
Grounding Foundational Vision Models with 3D Human Poses for Robust Action Recognition
For embodied agents to effectively understand and interact within the world around them, they require a nuanced comprehension of human actions grounded in physical space. Current action recognition models, often relying on RGB video, learn superficial correlations between patterns and action labels, so they struggle to capture underlying physical interaction dynamics and human poses in complex scenes. We propose a mo
Epistemic Reject Option Prediction
In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses this by allowing the model to abstain when prediction uncertainty is high. Traditional reject-option approaches focus solely on aleatoric uncertainty, an assumption valid only when large training data makes the epistemic uncertainty negligibl
Software Defined Vehicle Code Generation: A Few-Shot Prompting Approach
The emergence of Software-Defined Vehicles (SDVs) marks a paradigm shift in the automotive industry, where software now plays a pivotal role in defining vehicle functionality, enabling rapid innovation of modern vehicles. Developing SDV-specific applications demands advanced tools to streamline code generation and improve development efficiency. In recent years, general-purpose large language models (LLMs) have demon
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs to structural data, such as knowledge graphs or web graphs, remains a fundamental challenge. Some approaches adopt complex strategies to convert graphs into text sequences, resulting in significant token overhead and rendering them impract
Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However, we observe that combining these two orthogonal approaches o
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, and a modular, composable architecture for designing environments and training robot policies. Beyond physics and rendering, the framework integrates actuator models, mul
A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification
Antimicrobial peptides have emerged as promising molecules to combat antimicrobial resistance. However, fragmented datasets, inconsistent annotations, and the lack of standardized benchmarks hinder computational approaches and slow down the discovery of new candidates. To address these challenges, we present the Expanded Standardized Collection for Antimicrobial Peptide Evaluation (ESCAPE), an experimental framework
Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the dominant frequency components in downstream tasks and those represented during pretraining) as a key
Multimodal Diffusion Forcing for Forceful Manipulation
Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such methods often overlook the rich interplay between different modalities, i.e., sensory inputs, actions, and rewards, which is crucial for modeling robot behavior and understanding task outcomes. In this work, we propose Multimodal Diffusion Forci
Biomedical image segmentation is critical for precise structure delineation and downstream analysis. Traditional methods often struggle with noisy data, while deep learning models such as U-Net have set new benchmarks in segmentation performance. nnU-Net further automates model configuration, making it adaptable across datasets without extensive tuning. However, it requires a substantial amount of annotated data for
Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data
Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient outcomes. In this study, we analyzed pre-transplant electronic
Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of experts per input. However, their widespread deployment remains limited due to the high memory overhead associated with storing all expert parameters, particularly as the number of experts increases. To address this challenge, prior works have explored expert dropping and merging str
Generalist biomedical image segmentation models such as Cellpose are increasingly applied across diverse imaging modalities and cell types. However, two critical challenges remain underexplored: (1) the extent of training data redundancy and (2) the impact of cross domain transfer on model retention. In this study, we conduct a systematic empirical analysis of these challenges using Cellpose as a case study. First, t
Quantifying the Role of OpenFold Components in Protein Structure Prediction
Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the cont
Manhattan Distance Mapping (MDM) is a post-training deep neural network (DNN) weight mapping technique for memristive bit-sliced compute-in-memory (CIM) crossbars that reduces parasitic resistance (PR) nonidealities. PR limits crossbar efficiency by mapping DNN matrices into small crossbar tiles, reducing CIM-based speedup. Each crossbar executes one tile, requiring digital synchronization before the next layer. At t
Ask WhAI:Probing Belief Formation in Role-Primed LLM Agents
We present Ask WhAI, a systems-level framework for inspecting and perturbing belief states in multi-agent interactions. The framework records and replays agent interactions, supports out-of-band queries into each agent's beliefs and rationale, and enables counterfactual evidence injection to test how belief structures respond to new information. We apply the framework to a medical case simulator notable for its multi
Causal Structure and Representation Learning with Biomedical Applications
Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep learning applications, as it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. Although representation learning has been hugely successful in predictive tasks, it can f
Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models
Tokenization and transfer learning are two critical components in building state of the art time series foundation models for forecasting. In this work, we systematically study the effect of tokenizer design, specifically scaling and quantization strategies, on model performance, alongside the impact of pretraining versus random initialization. We show that tokenizer configuration primarily governs the representation
Bimanual and humanoid robots are appealing because of their human-like ability to leverage multiple arms to efficiently complete tasks. However, controlling multiple arms at once is computationally challenging due to the growth in the hybrid discrete-continuous action space. Task and Motion Planning (TAMP) algorithms can efficiently plan in hybrid spaces but generally produce plans, where only one arm is moving at a
CPO: Condition Preference Optimization for Controllable Image Generation
To enhance controllability in text-to-image generation, ControlNet introduces image-based control signals, while ControlNet++ improves pixel-level cycle consistency between generated images and the input control signal. To avoid the prohibitive cost of back-propagating through the sampling process, ControlNet++ optimizes only low-noise timesteps (e.g., $t < 200$) using a single-step approximation, which not only i
Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO), a novel me
Notable events recorded on this day and month across all years.