Record 15112025 · 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
The Election Commission of India (ECI) is a constitutional body that is empowered to conduct free and fair elections in India. Established by the Constitution of India, it is headed by a chief election commissioner and consists of two other election commission
The Bihar Legislative Assembly is the lower house of the bicameral Bihar Legislature of the state of Bihar in India. The first state elections were held in 1952.
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
2020 Bihar Legislative Assembly election
The Bihar Legislative Assembly election was held in three phases through October–November to elect members to the Seventeenth Bihar Legislative Assembly. The term of the previous Sixteenth Legislative Assembly of Bihar ended on 29 November 2020.
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.
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
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
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
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
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
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
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
Tejashwi Prasad Yadav is an Indian politician and former professional cricketer. He has previously served for two terms as the Deputy Chief Minister of Bihar and current leader of opposition in lower house. He is the youngest son of former Chief Ministers of B
Dharmendra was an Indian actor, producer and politician, primarily known for his work in Hindi films. He is regarded as one of the greatest and most commercially successful actors in the history of Indian cinema. Known as the "He-man", he was popular for his h
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
A series of coordinated Islamic terrorist attacks took place on Friday, 13 November 2015 in Paris, France, and the city's northern suburb, Saint-Denis. Beginning at 21:16, three suicide bombers struck outside the Stade de France in Saint-Denis, during an inter
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
The National Democratic Alliance (NDA) is an Indian multi-party political alliance, led by the country's biggest political party, the Bharatiya Janata Party (BJP). It was founded on 15 May 1998. It currently has a majority in both the Rajya Sabha and the Lok S
Sara Joanne Cyzer, better known by her maiden name Sara Cox, is an English radio DJ, television presenter and author. She hosted a range of BBC Radio 1 shows from 1999 to 2014, including the breakfast show from 2000 to 2003. She later joined Radio 2 and has ho
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
Saalumarada Thimmakka, also known as Aala Marada Thimmakka, was an Indian environmentalist from the state of Karnataka, noted for her work in planting and tending to 385 banyan trees along a 4.5-kilometre (2.8 mi) stretch of highway between Hulikal and Kudur,
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.
Janata Dal (United) (JD(U), lit. 'People's Party (United)'), is a social democratic and secular Indian political party, rooted mainly in eastern and north-eastern India, whose stated goals are promoting social justice and lifting up marginalised people. JD(U)
Virginia Lee Roberts Giuffre was an American and Australian advocate for survivors of sex trafficking and one of the most prominent accusers of Jeffrey Epstein. Giuffre provided detailed allegations to media outlets about Epstein and Ghislaine Maxwell. She all
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
Wuthering Heights is the only novel by the English author Emily Brontë, initially published in 1847 under her pen name Ellis Bell. It concerns two extensive upland estates and their landowning families on the West Yorkshire moors, the Earnshaws and the Lintons
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
From Phonemes to Meaning: Evaluating Large Language Models on Tamil
Large Language Models (LLMs) have shown strong generalization across tasks in high-resource languages; however, their linguistic competence in low-resource and morphologically rich languages such as Tamil remains largely unexplored. Existing multilingual benchmarks often rely on translated English datasets, failing to capture the linguistic and cultural nuances of the target language. To address this gap, we introduc
Cacheback: Speculative Decoding With Nothing But Cache
We present Cacheback Decoding, a training-free and model-agnostic speculative decoding method that exploits the locality in language to accelerate Large Language Model (LLM) inference. Cacheback leverages only Least Recently Used (LRU) cache tables of token n-grams to generate draft sequences. Cacheback achieves state-of-the-art performance among comparable methods despite its minimalist design, and its simplicity al
Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load
Negation instructions such as 'do not mention $X$' can paradoxically increase the accessibility of $X$ in human thought, a phenomenon known as ironic rebound. Large language models (LLMs) face the same challenge: suppressing a concept requires internally activating it, which may prime rebound instead of avoidance. We investigated this tension with two experiments. \textbf{(1) Load \& content}: after a negation in
Quantum Optimization Algorithms
Quantum optimization allows for up to exponential quantum speedups for specific, possibly industrially relevant problems. As the key algorithm in this field, we motivate and discuss the Quantum Approximate Optimization Algorithm (QAOA), which can be understood as a slightly generalized version of Quantum Annealing for gate-based quantum computers. We delve into the quantum circuit implementation of the QAOA, includin
Autonomous agents operating in sequential decision-making tasks under uncertainty can benefit from external action suggestions, which provide valuable guidance but inherently vary in reliability. Existing methods for incorporating such advice typically assume static and known suggester quality parameters, limiting practical deployment. We introduce a framework that dynamically learns and adapts to varying suggester r
More Than Irrational: Modeling Belief-Biased Agents
Despite the explosive growth of AI and the technologies built upon it, predicting and inferring the sub-optimal behavior of users or human collaborators remains a critical challenge. In many cases, such behaviors are not a result of irrationality, but rather a rational decision made given inherent cognitive bounds and biased beliefs about the world. In this paper, we formally introduce a class of computational-ration
Detecting anomalies in multivariate time series is essential for monitoring complex industrial systems, where high dimensionality, limited labeled data, and subtle dependencies between sensors cause significant challenges. This paper presents a deep reinforcement learning framework that combines a Variational Autoencoder (VAE), an LSTM-based Deep Q-Network (DQN), dynamic reward shaping, and an active learning module
CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Hyperspectral image (HSI) classification faces critical challenges, including high spectral dimensionality, complex spectral-spatial correlations, and limited training samples with severe class imbalance. While CNNs excel at local feature extraction and transformers capture long-range dependencies, their isolated application yields suboptimal results due to quadratic complexity and insufficient inductive biases. We p
Reward and Guidance through Rubrics: Promoting Exploration to Improve Multi-Domain Reasoning
Recent advances in reinforcement learning (RL) have significantly improved the complex reasoning capabilities of large language models (LLMs). Despite these successes, existing methods mainly focus on single-domain RL (e.g., mathematics) with verifiable rewards (RLVR), and their reliance on purely online RL frameworks restricts the exploration space, thereby limiting reasoning performance. In this paper, we address t
Ground Plane Projection for Improved Traffic Analytics at Intersections
Accurate turning movement counts at intersections are important for signal control, traffic management and urban planning. Computer vision systems for automatic turning movement counts typically rely on visual analysis in the image plane of an infrastructure camera. Here we explore potential advantages of back-projecting vehicles detected in one or more infrastructure cameras to the ground plane for analysis in real-
Learning Time in Static Classifiers
Real-world visual data rarely presents as isolated, static instances. Instead, it often evolves gradually over time through variations in pose, lighting, object state, or scene context. However, conventional classifiers are typically trained under the assumption of temporal independence, limiting their ability to capture such dynamics. We propose a simple yet effective framework that equips standard feedforward class
Decision and Gender Biases in Large Language Models: A Behavioral Economic Perspective
Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rati
GateRA: Token-Aware Modulation for Parameter-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, DoRA, and HiRA, enable lightweight adaptation of large pre-trained models via low-rank updates. However, existing PEFT approaches apply static, input-agnostic updates to all tokens, disregarding the varying importance and difficulty of different inputs. This uniform treatment can lead to overfitting on trivial content or under-adaptation on more informativ
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning. It consists of generating multiple responses, or ``samples", from a large language model (LLM) and selecting the most frequent answer. This procedure can naturally be viewed as a majority vote or empirical mode estimation. Despite its effectiveness, self-consistency is prohibitively expensive
As large language model (LLM) agents increasingly undertake digital work, reliable frameworks are needed to evaluate their real-world competence, adaptability, and capacity for human collaboration. Existing benchmarks remain largely static, synthetic, or domain-limited, providing limited insight into how agents perform in dynamic, economically meaningful environments. We introduce UpBench, a dynamically evolving benc
Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method
Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the risk of introducing detrimental features generated by AI and harming downstream tasks. This paper identifies the frequenc
Sangam: Chiplet-Based DRAM-PIM Accelerator with CXL Integration for LLM Inferencing
Large Language Models (LLMs) are becoming increasingly data-intensive due to growing model sizes, and they are becoming memory-bound as the context length and, consequently, the key-value (KV) cache size increase. Inference, particularly the decoding phase, is dominated by memory-bound GEMV or flat GEMM operations with low operational intensity (OI), making it well-suited for processing-in-memory (PIM) approaches. Ho
Large language models are increasingly influencing human moral decisions, yet current approaches focus primarily on evaluating rather than actively steering their moral decisions. We formulate this as an out-of-distribution moral alignment problem, where LLM agents must learn to apply consistent moral reasoning frameworks to scenarios beyond their training distribution. We introduce Moral-Reason-QA, a novel dataset e
Stabilizing Multi-Attack Adversarial Training via Bandit Optimization
Deep Neural Networks (DNNs) remain vulnerable to diverse adversarial perturbations, motivating multi-attack adversarial training (AT) for improved robustness. However, existing methods either incur prohibitive overhead by computing all attacks at each iteration, or rely on stochastic sampling over adversarial examples, which may cause excessive parameter drift. To address these issues, we propose Calibrated Adversari
Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare information across groups of videos. Most existing video understanding benchmarks focus on single-video analysis, failing to assess the ability of multimodal large language models (MLLMs) to simultaneously reason over various videos. Recent benchmar
Prompt-Conditioned FiLM and Multi-Scale Fusion on MedSigLIP for Low-Dose CT Quality Assessment
We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition patch-token features on clinical intent, enabling data-efficient learning and rapid adaptation. The architecture combines global, local, and texture-aware pooling through separate regression heads fused by a lightweight MLP, trained with pai
Mobile agents show immense potential, yet current state-of-the-art (SoTA) agents exhibit inadequate success rates on real-world, long-horizon, cross-application tasks. We attribute this bottleneck to the agents' excessive reliance on static, internal knowledge within MLLMs, which leads to two critical failure points: 1) strategic hallucinations in high-level planning and 2) operational errors during low-level executi
Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network
Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often lack interpretability and fail to generalize across noise regimes. In this study, we propose Deep Unfolded BM3D (DU-BM3D), a hybrid framework that unrolls BM3D into a trainable architecture by replacing its fixed collaborative filtering wi
Concept-Based Interpretability for Toxicity Detection
The rise of social networks has not only facilitated communication but also allowed the spread of harmful content. Although significant advances have been made in detecting toxic language in textual data, the exploration of concept-based explanations in toxicity detection remains limited. In this study, we leverage various subtype attributes present in toxicity detection datasets, such as obscene, threat, insult, ide
Unplanned extubation (UE) remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. We propose Augmented Unplanned Removal Alert (AURA), a vision-based risk detection system developed and validated entirely on a fully syntheti
Notable events recorded on this day and month across all years.