Record 03112025 · captured 2026-08-25
The world looked up Women's Cricket World Cup. 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.
The ICC Women's Cricket World Cup is the quadrennial international championship of the One Day International format with 50 overs per team. It is organised by the International Cricket Council.
Shohei Ohtani is a Japanese professional baseball designated hitter and pitcher for the Los Angeles Dodgers of Major League Baseball (MLB). Nicknamed "Shotime", he has previously played in MLB for the Los Angeles Angels and in Nippon Professional Baseball (NPB
Yoshinobu Yamamoto is a Japanese professional baseball pitcher for the Los Angeles Dodgers of Major League Baseball (MLB). He has previously played in Nippon Professional Baseball (NPB) for the Orix Buffaloes, where he became one of the most decorated pitchers
Maxwell Martin Scherzer, nicknamed "Mad Max", is an American professional baseball pitcher for the Toronto Blue Jays of Major League Baseball (MLB). He has previously played in MLB for the Arizona Diamondbacks, Detroit Tigers, Washington Nationals, Los Angeles
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
List of World Series champions
The World Series is the annual championship series of Major League Baseball (MLB) and concludes the MLB postseason. First played in 1903, the World Series championship is a best-of-seven playoff and is a contest between the champions of baseball's National Lea
William Dills Smith is an American professional baseball catcher for the Los Angeles Dodgers of Major League Baseball (MLB). He played college baseball for the Louisville Cardinals. He was selected by the Dodgers in the first round of the 2016 Major League Bas
The Los Angeles Dodgers are an American professional baseball team based in Los Angeles. The Dodgers compete in Major League Baseball (MLB) as a member club of the National League (NL) West Division. One of the most successful and storied franchises in MLB, th
Beauty trends among American conservatives
United States conservatives in the 2020s have demonstrated at least two notable beauty trends. The first is Republican makeup, also known as MAGA makeup, MAGA beauty or conservative girl makeup. This term describes the style and application of cosmetics by peo
Clayton Edward Kershaw is an American former professional baseball pitcher who played 18 seasons for the Los Angeles Dodgers of Major League Baseball (MLB). A left-handed starting pitcher, Kershaw was an 11-time National League (NL) All-Star, three-time Cy You
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 2025 World Series was the championship series of Major League Baseball's (MLB) 2025 season. The 121st edition of the World Series, it was a best-of-seven playoff between the National League (NL) champion and defending World Series champion Los Angeles Dodg
Smriti Mandhana is an Indian international cricketer and the vice-captain of the Indian women's national team. She was part of the Indian team that won the 2025 Women's Cricket World Cup, the Women's Asia Cup in 2016 and 2022. She also won a gold medal in the
The Toronto Blue Jays are a Canadian professional baseball team based in Toronto. The Blue Jays compete in Major League Baseball (MLB) as a member club of the American League (AL) East Division. Since 1989, the team has played its home games primarily at Roger
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
Vladimir Guerrero Ramos, Also known as Vladdy, is a Dominican-Canadian professional baseball first baseman for the Toronto Blue Jays of Major League Baseball (MLB). He made his MLB debut in 2019 and bats and throws right-handed. Guerrero is the son of Baseball
Harmanpreet Kaur Bhullar is an Indian cricketer who plays as an all-rounder and captains the India women's national team. She is a top order batter and a right-arm off-spin bowler. She captained the Indian team that won the 2025 Women's Cricket World Cup, the
Daylight saving time (DST), also referred to as daylight savings time, daylight time, or summer time, is the practice of advancing clocks to make better use of the longer daylight available during summer by having darkness fall at a later clock time. The typic
Markus Lynn "Mookie" Betts is an American professional baseball outfielder, shortstop, and second baseman for the Los Angeles Dodgers of Major League Baseball (MLB). He debuted in MLB for the Boston Red Sox. Betts is an eight-time All-Star, seven-time Silver S
Miguel Elias Rojas Naidenoff is a Venezuelan professional baseball infielder for the Los Angeles Dodgers of Major League Baseball (MLB). He has also played in MLB for the Miami Marlins. In 2025, he became the first player in MLB history to hit a game-tying hom
Dave Roberts (baseball manager)
David Ray Roberts, nicknamed "Doc", is an American professional baseball manager and former outfielder who is the manager of the Los Angeles Dodgers of Major League Baseball (MLB). He played for five MLB teams over a ten-year career and then coached for the Sa
Bo Joseph Bichette is a Brazilian - American professional baseball infielder for the New York Mets of Major League Baseball (MLB). He has previously played in MLB for the Toronto Blue Jays. Bichette was selected by the Blue Jays in the second round of the 2016
Saturday Night's Main Event is a series of American professional wrestling television specials produced by WWE. The series originally broadcast from 1985 to 1992, by NBC until 1991 then briefly by Fox. The specials were briefly revived on NBC from 2006 to 2008
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
It: Welcome to Derry is an American supernatural horror television series based on Stephen King's 1986 novel It. Serving as a prequel to the films It (2017) and It Chapter Two (2019), the series was developed by Andy Muschietti, Barbara Muschietti and Jason Fu
Ernie James Clement is an American professional baseball utility player for the Toronto Blue Jays of Major League Baseball (MLB). He has previously played in MLB for the Cleveland Indians/Cleveland Guardians and the Oakland Athletics. Internationally, he plays
Frederick Charles Freeman is a Canadian and American professional baseball first baseman for the Los Angeles Dodgers of Major League Baseball (MLB). Freeman made his MLB debut with the Atlanta Braves in 2010 and played with them for 12 seasons. After the Brave
Vladimir Guerrero Alvino, nicknamed "Vlad the Impaler", is a Dominican former professional baseball player who spent 16 seasons in Major League Baseball (MLB) as a right fielder and designated hitter. He played for the Montreal Expos (1996–2003), Anaheim Angel
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.
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.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking tokens. Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early stopping of unpromising reasoning chains, reducing compute by up to 26\% compa
Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models
Programming languages can benefit from one another by utilizing a language model for software engineering tasks. Full fine-tuning and Parameter Efficient Fine-Tuning (PEFT) of Code Language Models (Code-LMs) has been explored for multilingual knowledge transfer. AdapterFusion is a PEFT architecture that aims to enhance task performance by leveraging information from multiple programming languages, but primarily focus
Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape
In this paper we study how the choice of loss functions of non-convex optimization problems affects their robustness and optimization landscape, through the study of noisy matrix sensing. In traditional regression tasks, mean squared error (MSE) loss is a common choice, but it can be unreliable for non-Gaussian or heavy-tailed noise. To address this issue, we adopt a robust loss based on nonparametric regression, whi
Flood insurance is an effective strategy for individuals to mitigate disaster-related losses. However, participation rates among at-risk populations in the United States remain strikingly low. This gap underscores the need to understand and model the behavioral mechanisms underlying insurance decisions. Large language models (LLMs) have recently exhibited human-like intelligence across wide-ranging tasks, offering pr
Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences
We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misalig
Metamorphic Testing of Large Language Models for Natural Language Processing
Using large language models (LLMs) to perform natural language processing (NLP) tasks has become increasingly pervasive in recent times. The versatile nature of LLMs makes them applicable to a wide range of such tasks. While the performance of recent LLMs is generally outstanding, several studies have shown that they can often produce incorrect results. Automatically identifying these faulty behaviors is extremely us
Geometric Data Valuation via Leverage Scores
Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a geometric alternative based on statistical leverage scores, whi
Automated Reward Design for Gran Turismo
When designing reinforcement learning (RL) agents, a designer communicates the desired agent behavior through the definition of reward functions - numerical feedback given to the agent as reward or punishment for its actions. However, mapping desired behaviors to reward functions can be a difficult process, especially in complex environments such as autonomous racing. In this paper, we demonstrate how current foundat
Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have
Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling
The field of world modeling is fragmented, with researchers developing bespoke architectures that rarely build upon each other. We propose a framework that specifies the natural building blocks for structured world models based on the fundamental stochastic processes that any world model must capture: discrete processes (logic, symbols) and continuous processes (physics, dynamics); the world model is then defined by
Energy Loss Functions for Physical Systems
Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We de
Watermarking Discrete Diffusion Language Models
Watermarking has emerged as a promising technique to track AI-generated content and differentiate it from authentic human creations. While prior work extensively studies watermarking for autoregressive large language models (LLMs) and image diffusion models, it remains comparatively underexplored for discrete diffusion language models (DDLMs), which are becoming popular due to their high inference throughput. In this
Pinching Antennas Meet AI in Next-Generation Wireless Networks
Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas (PAs), a new flexible low-cost technology, can create line-of-sight links by dynamically activating small dielectric pinches along a waveguide on demand. As a compelling complement, a
Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing
Scientific experimentation and manufacturing rely on prolonged protocol development and complex, multi-step implementation, which require continuous human expertise for precise execution and decision-making, limiting interpretability and scalability. Here, we introduce human-artificial intelligence (AI) co-embodied intelligence, a new form of physical AI that unites human researchers, agentic AI, and wearable hardwar
There is growing interest in deploying ML inference and knowledge retrieval as services that could support both interactive queries by end users and more demanding request flows that arise from AIs integrated into a end-user applications and deployed as agents. Our central premise is that these latter cases will bring service level latency objectives (SLOs). Existing ML serving platforms use batching to optimize for
Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis
Creation of large-scale databases for Visual Question Answering tasks pertaining to the text data in a scene (text-VQA) involves skilful human annotation, which is tedious and challenging. With the advent of foundation models that handle vision and language modalities, and with the maturity of OCR systems, it is the need of the hour to establish an end-to-end pipeline that can synthesize Question-Answer (QA) pairs ba
Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We evaluate conversational response quality along three axes: naturalness, comprehensiveness, and on-topic adherence, each rated on 5-point scales. Using ensemble-generated data from multiple LLMs, we fine-tune a 7B-parameter model and test ac
Quantum-Enhanced Generative Models for Rare Event Prediction
Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical deep generative models often struggle to capture these rare occurrences, either collapsing low-probability modes or producing poorly calibrated uncertainty estimates. In this work, we propose the Quantum-Enhanced Generative Model (QEGM), a
RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients
Submodular maximization is an optimization problem benefiting many machine learning applications, where we seek a small subset best representing an extremely large dataset. We focus on the federated setting where the data are locally owned by decentralized clients who have their own definitions for the quality of representability. This setting requires repetitive aggregation of local information computed by the clien
Catastrophic forgetting is one of the fundamental issues of continual learning because neural networks forget the tasks learned previously when trained on new tasks. The proposed framework is a new path-coordinated framework of continual learning that unites the Neural Tangent Kernel (NTK) theory of principled plasticity bounds, statistical validation by Wilson confidence intervals, and evaluation of path quality by
Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior
Recent work has discovered that large language models can develop broadly misaligned behaviors after being fine-tuned on narrowly harmful datasets, a phenomenon known as emergent misalignment (EM). However, the fundamental mechanisms enabling such harmful generalization across disparate domains remain poorly understood. In this work, we adopt a geometric perspective to study EM and demonstrate that it exhibits a fund
Proof-of-Spiking-Neurons(PoSN): Neuromorphic Consensus for Next-Generation Blockchains
Blockchain systems face persistent challenges of scalability, latency, and energy inefficiency. Existing consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) either consume excessive resources or risk centralization. This paper proposes \textit{Proof-of-Spiking-Neurons (PoSN)}, a neuromorphic consensus protocol inspired by spiking neural networks. PoSN encodes transactions as spike trains, elects
InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations
In recent years, querying semantic web data using SPARQL has remained challenging, especially for non-expert users, due to the language's complex syntax and the prerequisite of understanding intricate data structures. To address these challenges, we propose InteracSPARQL, an interactive SPARQL query generation and refinement system that leverages natural language explanations (NLEs) to enhance user comprehension and
TRACE: Textual Reasoning for Affordance Coordinate Extraction
Vision-Language Models (VLMs) struggle to translate high-level instructions into the precise spatial affordances required for robotic manipulation. While visual Chain-of-Thought (CoT) methods exist, they are often computationally intensive. In this work, we introduce TRACE (Textual Reasoning for Affordance Coordinate Extraction), a novel methodology that integrates a textual Chain of Reasoning (CoR) into the affordan
Trove: A Flexible Toolkit for Dense Retrieval
We introduce Trove, an easy-to-use open-source retrieval toolkit that simplifies research experiments without sacrificing flexibility or speed. For the first time, we introduce efficient data management features that load and process (filter, select, transform, and combine) retrieval datasets on the fly, with just a few lines of code. This gives users the flexibility to easily experiment with different dataset config
Notable events recorded on this day and month across all years.