Record 16112025 · captured 2026-08-25
The world looked up 2025 Bihar Legislative Assembly election. 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.
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.
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
2026 FIFA World Cup qualification (UEFA)
The European section of the 2026 FIFA World Cup qualification competition acted as qualifiers for the 2026 FIFA World Cup, that was held in Canada, Mexico and the United States, for national teams that were members of the Union of European Football Association
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
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
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
2026 FIFA World Cup qualification
The 2026 FIFA World Cup qualification decided the 45 teams that joined hosts Canada, Mexico, and the United States at the 2026 FIFA World Cup.
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
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.
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
2018 Horizon Air Bombardier Q400 incident
On August 10, 2018, a Bombardier Q400 operated by Horizon Air was stolen from Seattle–Tacoma International Airport (Sea–Tac) by 28-year-old Richard Russell, a Horizon Air ground service agent with no piloting experience. After Russell performed an unauthorized
The Beast in Me is an American psychological crime thriller television miniseries for Netflix, starring Claire Danes and Matthew Rhys. Created by Gabe Rotter, the series follows an author (Danes) who begins writing a book about her new next-door neighbor (Rhys
Todd Daniel Snider was an American singer-songwriter whose music incorporated elements of folk, rock, blues, alt country and funk.
The Running Man is a 2025 science-fiction action film co-produced and directed by Edgar Wright, from a screenplay by Wright and Michael Bacall. It is the second adaptation of the 1982 novel by Stephen King, following the 1987 film. It stars Glen Powell as Ben
Maithili Thakur is an Indian playback singer trained in Indian classical music and folk music. She has sung original songs, covers, and traditional folk music prominently in Hindi, Bengali, Maithili, Urdu, Marathi, Bhojpuri, Punjabi, Tamil, English and more In
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Conor Nigel Benn is a British professional boxer. He is the son of former two-division world champion boxer Nigel Benn.
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
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.
Edward Regan Murphy is an American comedian, actor, and singer. He is widely recognized as one of the most influential Black artists in the entertainment industry, and one of the greatest comedians of all time. He had his breakthrough as a stand-up comic befor
Claire Catherine Danes is an American actor. Prolific in film and television since her teens, she is the recipient of three Primetime Emmy Awards and four Golden Globe Awards. In 2012 and 2026, Time named her one of the 100 most influential people in the world
Daria "Dasha" Dmitrievna Nekrasova is a Soviet-born American actress, filmmaker, and co-host of the Red Scare podcast with Anna Khachiyan, based in Dimes Square, New York City.
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
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.
Nitish Kumar is an Indian politician from Bihar. He is currently serving as a Member of Parliament in the Rajya Sabha. The national president of the Janata Dal (United), he was the longest serving Chief Minister of Bihar, serving briefly in 2000, from 2005 to
Bigg Boss (Tamil TV series) season 9
Bigg Boss 9 is the ninth season of the Indian Tamil-language reality television series Bigg Boss, produced by Banijay. Vijay Sethupathi is returning as a host for the second time in a row. The season premiered on 5 October 2025 on Star Vijay and JioHotstar. Un
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
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
Now You See Me: Now You Don't is a 2025 American heist film directed by Ruben Fleischer from a screenplay by Michael Lesslie, the writing duo of Paul Wernick and Rhett Reese, and Seth Grahame-Smith, based on a story by Eric Warren Singer and Lesslie. The film
Survivor Series: WarGames (2025)
The 2025 Survivor Series: WarGames, also promoted as Survivor Series: WarGames San Diego, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 39th annual Survivor Series and took place on November 29, 2025, at Pe
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2- and 3-node interactions, but at $\mathcal{O}(n^3)$ computational cost. However, this computational burden is typically mitigated by existing efficiency methods at the cost of reduced expressivity. We propose \textbf{Co-Sparsify}, a connectivity-aware sparsification framework that eliminates \emph{provably
SAGA: Source Attribution of Generative AI Videos
The proliferation of generative AI has led to hyper-realistic synthetic videos, escalating misuse risks and outstripping binary real/fake detectors. We introduce SAGA (Source Attribution of Generative AI videos), the first comprehensive framework to address the urgent need for AI-generated video source attribution at a large scale. Unlike traditional detection, SAGA identifies the specific generative model used. It u
From Passive to Persuasive: Localized Activation Injection for Empathy and Negotiation
Complex social behaviors, such as empathy and strategic politeness, are widely assumed to resist the directional decomposition that makes activation steering effective for coarse attributes like sentiment or toxicity. We present STAR: Steering via Attribution and Representation, which tests this assumption by using attribution patching to identify the layer--token positions where each behavioral trait causally origin
Catastrophic Forgetting in Kolmogorov-Arnold Networks
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategies have been proposed for Multi-Layer Perceptrons (MLPs), recent architectural advances like Kolmogorov-Arnold Networks (KANs) have been suggested to offer intrinsic resistance to forgetting by leveraging localized spline-based activations.
MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size. This task is further complicated by low-light conditions, partial occlusion, small object size, intricate background patterns, and multiple objects. While many sophisticated methods have been
Expressive Temporal Specifications for Reward Monitoring
Specifying informative and dense reward functions remains a pivotal challenge in Reinforcement Learning, as it directly affects the efficiency of agent training. In this work, we harness the expressive power of quantitative Linear Temporal Logic on finite traces (($\text{LTL}_f[\mathcal{F}]$)) to synthesize reward monitors that generate a dense stream of rewards for runtime-observable state trajectories. By providing
The Alignment Game: A Theory of Long-Horizon Alignment Through Recursive Curation
In self-consuming generative models that train on their own outputs, alignment with user preferences becomes a recursive rather than one-time process. We provide the first formal foundation for analyzing the long-term effects of such recursive retraining on alignment. Under a two-stage curation mechanism based on the Bradley-Terry (BT) model, we model alignment as an interaction between two factions: the Model Owner,
Genomic Next-Token Predictors are In-Context Learners
In-context learning (ICL) -- the capacity of a model to infer and apply abstract patterns from examples provided within its input -- has been extensively studied in large language models trained for next-token prediction on human text. In fact, prior work often attributes this emergent behavior to distinctive statistical properties in human language. This raises a fundamental question: can ICL arise organically in ot
Maximizing the efficiency of human feedback in AI alignment: a comparative analysis
Reinforcement Learning from Human Feedback (RLHF) relies on preference modeling to align machine learning systems with human values, yet the popular approach of random pair sampling with Bradley-Terry modeling is statistically limited and inefficient under constrained annotation budgets. In this work, we explore alternative sampling and evaluation strategies for preference inference in RLHF, drawing inspiration from
Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on designing novel network architectures for individual prob- lems, less effort has been devoted to exploring new learning paradigms involving a sequence of problems. In this work, we investigate lifelong learning ILP, which leverages the com- po
Multi-Agent Reinforcement Learning for Heterogeneous Satellite Cluster Resources Optimization
This work investigates resource optimization in heterogeneous satellite clusters performing autonomous Earth Observation (EO) missions using Reinforcement Learning (RL). In the proposed setting, two optical satellites and one Synthetic Aperture Radar (SAR) satellite operate cooperatively in low Earth orbit to capture ground targets and manage their limited onboard resources efficiently. Traditional optimization metho
Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentralized, heterogeneous, and often non-independent. While recent work has explored horizon selection by preserving forecasting-relevant information in an intrinsic space, these approaches are primarily restricted to centralized and independent
Lightweight Optimal-Transport Harmonization on Edge Devices
Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem naturally arises in augmented reality (AR), yet harmonization algorithms are not currently integrated into AR pipelines because real-time solutions are scarce. In this work, we address color harmonization for AR by proposing a lightweight appr
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chain-of-thought responses to real oncology notes and tested its
Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation
We study the problem of efficiently estimating policies that simultaneously optimize multiple objectives in reinforcement learning (RL). Given $n$ objectives (or tasks), we seek the optimal partition of these objectives into $k \ll n$ groups, where each group comprises related objectives that can be trained together. This problem arises in applications such as robotics, control, and preference optimization in languag
Modeling Fairness in Recruitment AI via Information Flow
Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to
While spatio-temporal Graph Neural Networks (GNNs) excel at modeling recurring traffic patterns, their reliability plummets during non-recurring events like accidents. This failure occurs because GNNs are fundamentally correlational models, learning historical patterns that are invalidated by the new causal factors introduced during disruptions. To address this, we propose Event-CausNet, a framework that uses a Large
Evidence of Phase Transitions in Small Transformer-Based Language Models
Phase transitions have been proposed as the origin of emergent abilities in large language models (LLMs), where new capabilities appear abruptly once models surpass critical thresholds of scale. Prior work, such as that of Wei et al., demonstrated these phenomena under model and data scaling, with transitions revealed after applying a log scale to training compute. In this work, we ask three complementary questions:
Human memory retrieval often resembles ecological foraging where animals search for food in a patchy environment. Optimal foraging means following the Marginal Value Theorem (MVT), in which individuals exploit a patch of semantically related concepts until it becomes less rewarding and then switch to a new cluster. While human behavioral data suggests foraging-like patterns in semantic fluency tasks, it remains uncle
Which Way from B to A: The role of embedding geometry in image interpolation for Stable Diffusion
It can be shown that Stable Diffusion has a permutation-invariance property with respect to the rows of Contrastive Language-Image Pretraining (CLIP) embedding matrices. This inspired the novel observation that these embeddings can naturally be interpreted as point clouds in a Wasserstein space rather than as matrices in a Euclidean space. This perspective opens up new possibilities for understanding the geometry of
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real time, as individuals often have unique preferences and policies that may change dynamically throughout interactions. This becomes particularly challenging in tasks with time pressure and complex strategic spaces, where identifying partner beha
Whose Narrative is it Anyway? A KV Cache Manipulation Attack
The Key Value(KV) cache is an important component for efficient inference in autoregressive Large Language Models (LLMs), but its role as a representation of the model's internal state makes it a potential target for integrity attacks. This paper introduces "History Swapping," a novel block-level attack that manipulates the KV cache to steer model generation without altering the user-facing prompt. The attack involve
Autonomous vehicle navigation in complex environments such as dense and fast-moving highways and merging scenarios remains an active area of research. A key limitation of RL is its reliance on well-specified reward functions, which often fail to capture the full semantic and social complexity of diverse, out-of-distribution situations. As a result, a rapidly growing line of research explores using Large Language Mode
Opinion Mining and Analysis Using Hybrid Deep Neural Networks
Understanding customer attitudes has become a critical component of decision-making due to the growing influence of social media and e-commerce. Text-based opinions are the most structured, hence playing an important role in sentiment analysis. Most of the existing methods, which include lexicon-based approaches and traditional machine learning techniques, are insufficient for handling contextual nuances and scalabil
XAI-Driven Deep Learning for Protein Sequence Functional Group Classification
Proteins perform essential biological functions, and accurate classification of their sequences is critical for understanding structure-function relationships, enzyme mechanisms, and molecular interactions. This study presents a deep learning-based framework for functional group classification of protein sequences derived from the Protein Data Bank (PDB). Four architectures were implemented: Convolutional Neural Netw
Notable events recorded on this day and month across all years.