Record 10112025 · captured 2026-08-25
The world looked up Frankenstein (2025 film). 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.
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
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
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
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
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
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.
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
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.
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
Nicole Rene Glaser is an American stand-up comedian and actress. She has had five television stand-up specials, hosted numerous award shows, and performed at numerous televised roasts, gaining significant popularity for her set on The Roast of Tom Brady. Previ
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
Megan Martha White is an American musician who was the drummer and occasional vocalist of the rock duo the White Stripes. She was a key artist of the 2000s indie and garage rock movements, noted for her minimalist drumming style and reserved public persona. Th
Mia Gypsy Mello da Silva Goth is a British actress and model. After modelling as a teenager, Goth made her acting debut in the erotic drama Nymphomaniac (2013). She earned recognition with the films A Cure for Wellness (2016), High Life (2018), Suspiria (2018)
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.
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
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
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.
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.
Paul John Tagliabue was an American lawyer who was the commissioner of the National Football League (NFL). He took the position in 1989 and served until September 1, 2006. He had previously served as a lawyer for the NFL.
Claudia Anne Irena Winkleman is an English broadcaster and writer. She co-presented the BBC One dance competition Strictly Come Dancing (2010–2025) and hosts the BBC One reality series The Traitors (2022–present), the latter of which won her a BAFTA award in 2
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 Formula One World Championship
The 2025 FIA Formula One World Championship was a motor racing championship for Formula One cars and the 76th running of the Formula One World Championship. It was recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of interna
All Her Fault is a mystery thriller television miniseries for Peacock starring Sarah Snook, Jake Lacy, and Dakota Fanning. It is based on the 2021 novel of the same name by Andrea Mara. The series premiered on November 6, 2025, and received generally positive
Shane Michael Boose, known professionally as Sombr, is an American singer-songwriter and musician. He released his debut single, "Nothing Left to Say", in 2021 and his first extended play (EP) In Another Life in 2023, through Warner Records and his own imprint
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.
League of Legends World Championship
The League of Legends World Championship is the annual professional League of Legends world championship tournament hosted by Riot Games and is the culmination of each season. Teams compete for the champion title, the 22-pound (10-kilogram) Summoner's Cup, and
The Hong Kong Sixes is an international six-a-side cricket tournament held annually in Hong Kong. Organised by Cricket Hong Kong, China and sanctioned by International Cricket Council, it features short-format matches designed to promote fast-paced, high-scori
XXX may refer to:
Death by Lightning is an American historical drama miniseries created by Mike Makowsky and based on the 2011 book Destiny of the Republic by Candice Millard. It stars Michael Shannon as United States President James A. Garfield and Matthew Macfadyen as his ass
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Diffusion Guided Adversarial State Perturbations in Reinforcement Learning
Reinforcement learning (RL) systems, while achieving remarkable success across various domains, are vulnerable to adversarial attacks. This is especially a concern in vision-based environments where minor manipulations of high-dimensional image inputs can easily mislead the agent's behavior. To this end, various defenses have been proposed recently, with state-of-the-art approaches achieving robust performance even u
CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences
Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet effective alternative, Confidence-Aware Preference Optimizati
Towards AI-Assisted Generation of Military Training Scenarios
Achieving expert-level performance in simulation-based training relies on the creation of complex, adaptable scenarios, a traditionally laborious and resource intensive process. Although prior research explored scenario generation for military training, pre-LLM AI tools struggled to generate sufficiently complex or adaptable scenarios. This paper introduces a multi-agent, multi-modal reasoning framework that leverage
Stress Testing Factual Consistency Metrics for Long-Document Summarization
Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, where conventional metrics struggle with input length limitations and long-range dependencies. In this work, we systematically evaluate the reliability of six widely used reference-free factuality metrics, originally proposed for short-form summarization, in the long-document setting.
ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents
Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Evaluating DR remains challenging because responses are lengthy and diverse, admit many valid solutions, and often depend on
Designing and Evaluating Malinowski's Lens: An AI-Native Educational Game for Ethnographic Learning
This study introduces 'Malinowski's Lens', the first AI-native educational game for anthropology that transforms Bronislaw Malinowski's 'Argonauts of the Western Pacific' (1922) into an interactive learning experience. The system combines Retrieval-Augmented Generation with DALL-E 3 text-to-image generation, creating consistent VGA-style visuals as players embody Malinowski during his Trobriand Islands fieldwork (191
AIA Forecaster: Technical Report
This technical report describes the AIA Forecaster, a Large Language Model (LLM)-based system for judgmental forecasting using unstructured data. The AIA Forecaster approach combines three core elements: agentic search over high-quality news sources, a supervisor agent that reconciles disparate forecasts for the same event, and a set of statistical calibration techniques to counter behavioral biases in large language
Speech Separation for Hearing-Impaired Children in the Classroom
Classroom environments are particularly challenging for children with hearing impairments, where background noise, multiple talkers, and reverberation degrade speech perception. These difficulties are greater for children than adults, yet most deep learning speech separation models for assistive devices are developed using adult voices in simplified, low-reverberation conditions. This overlooks both the higher spectr
Retrieval-Augmented Generation (RAG) based chatbots are not only useful for information retrieval through questionanswering but also for making complex decisions based on injected private data.we present a survey on how much search time can be saved when retrieving complex information within an organization called "X Systems"(a stealth mode company) by using a RAG-based chatbot compared to traditional search methods.
Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes
The equitable assessment of individual contribution in teams remains a persistent challenge, where conflict and disparity in workload can result in unfair performance evaluation, often requiring manual intervention - a costly and challenging process. We survey existing tool features and identify a gap in conflict resolution methods and AI integration. To address this, we propose a framework and implementation design
FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing
Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory acces
Cortex AISQL: A Production SQL Engine for Unstructured Data
Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at production scale poses fundamental challenges. Semantic operati
Evaluating answers from state-of-the-art large language models (LLMs) is challenging: lexical metrics miss semantic nuances, whereas "LLM-as-Judge" scoring is computationally expensive. We re-evaluate a lightweight alternative -- off-the-shelf Natural Language Inference (NLI) scoring augmented by a simple lexical-match flag and find that this decades-old technique matches GPT-4o's accuracy (89.9%) on long-form QA, wh
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for multi-reservoir inflow forecasting. AdaTrip constructs dynamic graphs where reservoirs are nodes with directed edges reflecting hydrological
A Self-Improving Architecture for Dynamic Safety in Large Language Models
Context: Large Language Models (LLMs) rely on static, pre-deployment safety mechanisms that cannot adapt to adversarial threats discovered after release. Objective: To design a software architecture enabling LLM-based systems to autonomously detect safety failures and synthesize defense policies at runtime, without retraining or manual intervention. Method: We propose the Self-Improving Safety Framework (SISF), groun
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving documents from an external corpus at inference time. When this corpus contains sensitive information, however, unprotected RAG systems are at risk of leaking private information. Prior work has introduced differential privacy (DP) guarantees for RAG, but only in single-query settings, which fall short of realistic usage. In this
Partial Action Replacement: Tackling Distribution Shift in Offline MARL
Offline multi-agent reinforcement learning (MARL) is severely hampered by the challenge of evaluating out-of-distribution (OOD) joint actions. Our core finding is that when the behavior policy is factorized - a common scenario where agents act fully or partially independently during data collection - a strategy of partial action replacement (PAR) can significantly mitigate this challenge. PAR updates a single or part
One Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers
A common practice in heterogeneous graph neural networks (HGNNs) is to condition parameters on node/edge types, assuming types reflect semantic roles. However, this can cause overreliance on surface-level labels and impede cross-type knowledge transfer. We explore integrating Mixture-of-Experts (MoE) into HGNNs--a direction underexplored despite MoE's success in homogeneous settings. Crucially, we question the need f
Leveraging the Power of AI and Social Interactions to Restore Trust in Public Polls
The emergence of crowdsourced data has significantly reshaped social science, enabling extensive exploration of collective human actions, viewpoints, and societal dynamics. However, ensuring safe, fair, and reliable participation remains a persistent challenge. Traditional polling methods have seen a notable decline in engagement over recent decades, raising concerns about the credibility of collected data. Meanwhile
Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces
Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for
LLM Output Drift: Cross-Provider Validation & Mitigation for Financial Workflows
Financial institutions deploy Large Language Models (LLMs) for reconciliations, regulatory reporting, and client communications, but nondeterministic outputs (output drift) undermine auditability and trust. We quantify drift across five model architectures (7B-120B parameters) on regulated financial tasks, revealing a stark inverse relationship: smaller models (Granite-3-8B, Qwen2.5-7B) achieve 100% output consistenc
Large language models (LLMs) have transformed software development by enabling automated code generation, yet they frequently suffer from systematic errors that limit practical deployment. We identify two critical failure modes: \textit{logical hallucination} (incorrect control/data-flow reasoning) and \textit{schematic hallucination} (type mismatches, signature violations, and architectural inconsistencies). These e
Think Before You Retrieve: Learning Test-Time Adaptive Search with Small Language Models
Effective information retrieval requires reasoning over partial evidence and refining strategies as information emerges. Yet current approaches fall short: neural retrievers lack reasoning capabilities, large language models (LLMs) provide semantic depth but at prohibitive cost, and query rewriting or decomposition limits improvement to static transformations. As a result, existing methods fail to capture the iterati
Procedural Knowledge Improves Agentic LLM Workflows
Large language models (LLMs) often struggle when performing agentic tasks without substantial tool support, prom-pt engineering, or fine tuning. Despite research showing that domain-dependent, procedural knowledge can dramatically increase planning efficiency, little work evaluates its potential for improving LLM performance on agentic tasks that may require implicit planning. We formalize, implement, and evaluate an
Notable events recorded on this day and month across all years.