Record 12122025 · captured 2026-08-25
The world looked up Dhurandhar. 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.
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Sherrone Banfield Moore is an American former college football head coach and player. He most recently served as the head football coach for the University of Michigan. Moore served as Michigan's acting head coach in four games during the national championship
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
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
Philip Michael Rivers is an American former professional football quarterback who played in the National Football League (NFL) for 18 seasons, primarily with the Chargers franchise. He played college football for the NC State Wolfpack, winning ACC Player of th
Sardar Abdul Rehman Baloch, known by the alias Rehman Dakait, was a Pakistani gangster based in Karachi's Lyari neighbourhood who formed the Peoples' Aman Committee which was affiliated with the Pakistan People's Party. The Government of Sindh had set a reward
Andrew Roane Dick is an American actor and comedian. Dick was born in Charleston, South Carolina, and joined The Second City and studied improvisational theater. Dick has had a long career as a stand-up comedian; he has appeared throughout the United States, h
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.
Madeleine Sophie Wickham, known by her pen name Sophie Kinsella, was an English author. She is especially known for her best-selling Shopaholic series of novels. By the time of her death, her books had sold over 50 million copies in more than 60 countries and
Sean John Combs, also known professionally as Diddy, is an American former rapper, record producer, record executive, and actor. He is credited with the discovery and development of musical artists such as the Notorious B.I.G., Mary J. Blige, and Usher, among
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Akshaye Vinod Khanna is an Indian actor who predominantly works in Hindi films. Known for his acting versatility and strong portrayals, he has appeared in over 40 films. Khanna is often regarded as one of the finest actors in Hindi cinema. He is a recipient of
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
Aditya Dhar is an Indian filmmaker who works in Hindi cinema. Having previously worked as a lyricist, Dhar made his directorial debut with the 2019 war film Uri: The Surgical Strike, a commercially successful venture which earned him the National Film Award fo
Major Mohit Sharma was an Indian Army Officer who was posthumously awarded the Ashoka Chakra, India's highest peace-time military decoration. Sharma was from the elite 1st Para SF.
Sara Arjun is an Indian actress who primarily appears in Tamil and Hindi films. The daughter of actor Raj Arjun, she appeared in several television commercials, including advertisements for Clinic Plus, and a short Hindi film before the age of six. She gained
Supergirl is a 2026 American superhero film based on the DC Comics superheroine Kara Zor-El / Supergirl. Directed by Craig Gillespie and written by Ana Nogueira, it is the second film in the DC Universe (DCU). Milly Alcock stars in the title role, alongside Ma
Jeffrey Garcia was an American stand-up comedian and actor. In animation, he voiced Sheen Estevez in Jimmy Neutron: Boy Genius as well as its two Nickelodeon spin-off television show series—The Adventures of Jimmy Neutron, Boy Genius and Planet Sheen—along wit
The 2025 SEA Games, officially called the 33rd SEA Games was an international multi-sport event sanctioned by the Southeast Asian Games Federation (SEAGF). The event took place in December 2025 from 9 to 20 December and was held across the Bangkok Metropolitan
María Corina Machado Parisca is a Venezuelan politician, activist, and prominent leader of the opposition to the administrations of Hugo Chávez, Nicolás Maduro, and Delcy Rodríguez. She served as a member of the National Assembly of Venezuela from 2011 to 2014
Casandra Elizabeth Ventura is an American singer, dancer, actress, and model. Born in New London, Connecticut, she began her musical career in 2004 after meeting producer Ryan Leslie, who signed her to his record label, NextSelection Lifestyle Group. She was t
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.
Stuart Orlando Scott was an American sportscaster and anchor on ESPN, including on SportsCenter. Known for his hip-hop style and use of catchphrases, Scott was also a regular for the network in its National Basketball Association (NBA) and National Football Le
Aslam Khan, better known as Chaudhary Aslam, was a Pakistani police officer in the Sindh Police. He was known for his involvement in several encounter killings of criminals and militants. On 9 January 2014, he was killed in a suicide car bombing carried out by
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
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
James Kevin Ward was an American voice actor and radio personality. He was best known for his roles as Doug Dimmadome and Chet Ubetcha in The Fairly OddParents (2001–2017), Captain Qwark in the Ratchet & Clank franchise (2002–2021), and XLR8, Diamondhead, and
Johan Jordi Cruijff is a Dutch-Spanish professional football director, coach and former player. Following an appointment in December 2025, he is the Technical Director at Ajax.
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
Uzair Jan Baloch is a Pakistani gangster and former crime lord. He was also the head of the outlawed Peoples' Aman Committee based in Lyari, Karachi, Sindh, Pakistan.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Dual Cluster Contrastive learning for Object Re-Identification
Recently, cluster contrastive learning has been proven effective for object ReID by computing the contrastive loss between the individual features and the cluster memory. However, existing methods that use the individual features to momentum update the cluster memory will fluctuate over the training examples, especially for the outlier samples. Unlike the individual-based updating mechanism, the centroid-based updati
Combining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketching
In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks. Our approach uses Monte-Carlo simulations to update the solution and re-estimates the new solution from samples as a tensor-network using a recently proposed tensor train sketching technique. We showcase the versatility and flexibility of our approach by applying it to two specific scenarios
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning
Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem either continuously replan paths using heuristic search methods to avoid conflicts or choose appropriate collision avoidance strategies based on learning approaches. The form
Joint2Human: High-quality 3D Human Generation via Compact Spherical Embedding of 3D Joints
3D human generation is increasingly significant in various applications. However, the direct use of 2D generative methods in 3D generation often results in losing local details, while methods that reconstruct geometry from generated images struggle with global view consistency. In this work, we introduce Joint2Human, a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly, en
Noisy Spiking Actor Network for Exploration
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies. Spiking neural networks (SNNs), due to their binary firing mechanism, have strong robustness to noise, making it difficult to realize efficient exploration with local disturbances. To solve this exploration problem, we propose a noisy spiking actor network (NoisySAN) that introduces t
Mental health disorders affect a significant portion of the global population, with diagnoses primarily conducted through Mental State Examinations (MSEs). MSEs serve as structured assessments to evaluate behavioral and cognitive functioning across various domains, aiding mental health professionals in diagnosis and treatment monitoring. However, in developing countries, access to mental health support is limited, le
PHLP: Sole Persistent Homology for Link Prediction - Interpretable Feature Extraction
Link prediction (LP), inferring the connectivity between nodes, is a significant research area in graph data, where a link represents essential information on relationships between nodes. Although graph neural network (GNN)-based models have achieved high performance in LP, understanding why they perform well is challenging because most comprise complex neural networks. We employ persistent homology (PH), a topologic
ObjectAdd: Adding Objects into Image via a Training-Free Diffusion Modification Fashion
We introduce ObjectAdd, a training-free diffusion modification method to add user-expected objects into user-specified area. The motive of ObjectAdd stems from: first, describing everything in one prompt can be difficult, and second, users often need to add objects into the generated image. To accommodate with real world, our ObjectAdd maintains accurate image consistency after adding objects with technical innovatio
The Spatial Semantics of Iconic Gesture
The current multimodal turn in linguistic theory leaves a crucial question unanswered: what is the meaning of iconic gestures, and how does it compose with speech meaning? We argue for a separation of linguistic and visual levels of meaning and introduce a spatial gesture semantics that closes this gap. Iconicity is differentiated into three aspects: Firstly, an interpretation of the form of a gesture in terms of a t
Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between multiple instructions, (ii) positional bias where the order of instructions affects model performance, and (iii) a lack of objectively verifiable tasks. To address these issues, we introduce a benchmark designed to evaluate models' abilitie
Anthropocentric bias in language model evaluation
Evaluating the cognitive capacities of large language models (LLMs) requires overcoming not only anthropomorphic but also anthropocentric biases. This article identifies two types of anthropocentric bias that have been neglected: overlooking how auxiliary factors can impede LLM performance despite competence ("auxiliary oversight"), and dismissing LLM mechanistic strategies that differ from those of humans as
Machine Learning for Quantifier Selection in cvc5
In this work we considerably improve the state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each qu
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials
Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance in the text generation task, showing the ability to understand and respond to complex instructions. However, the performance of naive LLMs in speciffc domains is limited due to the scarcity of domain-speciffc corpora and specialized training. Moreover, training a specialized large-scale model necessitates signiffcant hardware reso
Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, re
How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method
Temporal Heterogeneous Networks play a crucial role in capturing the dynamics and heterogeneity inherent in various real-world complex systems, rendering them a noteworthy research avenue for link prediction. However, existing methods fail to capture the fine-grained differential distribution patterns and temporal dynamic characteristics, which we refer to as spatial heterogeneity and temporal heterogeneity. To overc
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization
Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional poisoning attacks are not as threatening as commonly believed. This is because they often cause differences in how the model performs on the training set compared to the validation set. Such inconsistency can alert defenders that their data h
l0-Regularized Sparse Coding-based Interpretable Network for Multi-Modal Image Fusion
Multi-modal image fusion (MMIF) enhances the information content of the fused image by combining the unique as well as common features obtained from different modality sensor images, improving visualization, object detection, and many more tasks. In this work, we introduce an interpretable network for the MMIF task, named FNet, based on an $\ell_0$-regularized multi-modal convolutional sparse coding (MCSC) model. Spe
Equivariant Test-Time Training with Operator Sketching for Imaging Inverse Problems
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant computational redundancy leading to inefficiency in high-dimensional applications, we propose a sketched EI regularization which leverages the randomized sketching techni
Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation
Recent work suggests that large language models enhanced with retrieval-augmented generation are easily influenced by the order, in which the retrieved documents are presented to the model when solving tasks such as question answering (QA). However, there is no method to date that exploits this phenomenon to improve generation. We fill this gap. In this study, we show that the pointwise mutual information between a c
A Generation Framework with Strict Constraints for Crystal Materials Design
The design of crystal materials plays a critical role in areas such as new energy development, biomedical engineering, and semiconductors. Recent advances in data-driven methods have enabled the generation of diverse crystal structures. However, most existing approaches still rely on random sampling without strict constraints, requiring multiple post-processing steps to identify stable candidates with the desired phy
Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion
Multimodal image fusion (MMIF) integrates information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing research focuses on complementary information fusion and training strategies, overlooking the critical role of underlying architectural components like normalization and convolution kernels. We reevaluate the UNet architecture for end-to-end MMIF, identifying that
Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA, (Fashion Language Outfit Representation for Apparel Generation), the first comprehensive dataset containing 4,330 curated pairs of fashion outfits and corresponding textual descriptions. Each description utilizes industry-specific terminology and jargon comm
Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints
As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic speech and attributing it to its source model. Our method addresses three tasks: (1) single-model attribution in an open-world setting, (2) multi-model attribution in a closed-world setting, and (3) real vs. synthetic speech classification. The
Despite the rapid pace at which deep networks are improving on standardized vision benchmarks, they are still outperformed by humans on real-world vision tasks. One solution to this problem is to make deep networks more brain-like. Although there are several benchmarks that compare the ability of deep networks to predict brain responses on natural images, they do not capture subtle but important emergent properties p
Object-centric proto-symbolic behavioural reasoning from pixels
Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels -- ideally without requiring supervision in the form of
Notable events recorded on this day and month across all years.