Record 16102026 · captured 2026-10-17
The world looked up GBU-43/B MOAB. 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.
The GBU-43/B Massive Ordnance Air Blast is a large-yield bomb, developed for the United States military by Albert L. Weimorts, Jr. of the Air Force Research Laboratory. It was first tested in 2003. At the time of development, it was said to be the most powerfu
Kate Sharon Zacharia (née Black) is an American political commentator who served as the 37th White House press secretary in October 2026. She served for only five days, making her the shortest-serving White House press secretary in U.S. history.
Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar. It was produced by Kalanithi Maran through Sun Pictures. A sequel to Jailer (2023), the film stars Rajinikanth, Ramya Krishnan, Yogi Babu, Mirnaa and Rithvik
Bigg Boss (Hindi TV series) season 20
Bigg Boss 20, also known as Bigg Boss: Tathas-two, is the twentieth season of the Indian Hindi-language reality television series Bigg Boss. It premiered on 6 September 2026 on Colors TV and streams digitally on JioHotstar. Salman Khan is hosting the show for
Roy Frank "RJ" Mitte III was an American actor and disability rights activist. Born in Jackson, Mississippi, Mitte was diagnosed with cerebral palsy when he was three years old. He moved to Los Angeles in 2006 and worked with a personal talent manager to find
Mathematics is a field of knowledge concerned with abstract concepts such as numbers, geometric shapes, sets, functions, and probabilities. It uses logical reasoning and proof to study and establish their properties, often expressed as theorems, formulas, and
This is a list of equations, indicating each Wikipedia page under appropriate bands of their field.
Imran Khan is an American actor and director of Indian origin, known for his work in Hindi-language films. He has received several accolades, including a Filmfare Award, and is known for his work across a range of film genres.
Nidal Malik Hasan is an American mass murderer and physician, who previously served as a United States Army major and perpetrated the 2009 Fort Hood shooting, in which he murdered 13 people and injured 32 others.
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
VisionQuest is an American television miniseries created by Terry Matalas for the streaming service Disney+, based on Marvel Comics featuring the character Vision. A spin-off from the miniseries WandaVision (2021), it is the 18th television series in the Marve
Street Fighter is a 2026 martial arts comedy film directed by Kitao Sakurai and written by Sakurai and T. J. Fixman, with a story by Dalan Musson and Gary Dauberman. It is the third live-action feature-length film based on the Street Fighter video game series
Jeffrey Preston Bezos is an American businessman, and the founder, executive chairman, and former president and CEO of Amazon, the world's largest e-commerce and cloud computing company. According to the Bloomberg Billionaires Index and Forbes, he was the worl
Kimberly Ann Guilfoyle is an American media personality and politician who served as the United States ambassador to Greece from 2025 until her resignation in 2026. She previously served as an advisor and led the fundraising division of the Donald Trump 2020 p
Carrie is an American supernatural horror television miniseries developed by Mike Flanagan, and produced by Amazon MGM Studios for Amazon Prime Video. The first series adaptation of the eponymous franchise, it is based on the 1974 novel by Stephen King, and is
James Dell Talarico is an American politician, Presbyterian seminarian, and educator serving since 2018 as a member of the Texas House of Representatives. He is the Democratic nominee in the 2026 U.S. Senate election in Texas.
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
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'.
Neatsville is an unincorporated community in Adair County, located 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
Below is a 2026 Canadian thriller miniseries created by Jesse McKeown and starring Josh Hartnett, Mackenzie Davis, and Charlie Heaton. It premiered on Netflix on October 8, 2026. The series received generally positive reviews from critics.
Crystal Lake is a American slasher television series developed by Brad Caleb Kane that is based on the Friday the 13th film franchise. It is a prequel to the 1980 film and stars Linda Cardellini in the leading role of Pamela Voorhees, with Callum Vinson as a y
Christopher Michael Coke, also known as Dudus, is a convicted Jamaican drug lord and the leader of the Shower Posse, a violent drug gang started by his father Lester Coke in Jamaica, which exported "large quantities" of marijuana and cocaine into the United St
Hilary Ann Swank is an American actress and film producer. Known for her leading roles in film, she has received numerous accolades including two Academy Awards, an Actor Award, two Critics' Choice Awards, and two Golden Globe Awards as well as nomination for
Jakob Christopher Bauers is an American professional baseball first baseman and left fielder for the Milwaukee Brewers of Major League Baseball (MLB). He has previously played in MLB for the Tampa Bay Rays, Cleveland Indians, Seattle Mariners, and New York Yan
Whalefall is a 2026 American survival thriller film directed and produced by Brian Duffield, based on the 2023 novel by Daniel Kraus, who co-wrote the script with Duffield. The film follows a scuba diver who, while searching for the remains of his estranged fa
Ansel Easton Adams was an American landscape photographer and environmentalist known for his black-and-white images of the American West. He helped found Group f/64, an association of photographers advocating "pure" photography which favored sharp focus and th
Karoline Claire Leavitt is an American political spokesperson who served as the 36th White House press secretary from 2025 to 2026. A member of the Republican Party, she was the party's nominee in the 2022 election for New Hampshire's 1st congressional distric
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Lizzie Andrew Borden was an American woman who was tried and acquitted of the murders of her father and stepmother in Fall River, Massachusetts. No other individuals were charged with the murders.
Love of Your Life is a 2026 American romantic drama film directed by Rachel Morrison, from a script by Julia Cox. It stars Margaret Qualley, Aaron Pierre, Patrick Schwarzenegger, Gabriel Basso, and Catherine Keener. It is produced by Ryan Gosling and Jessie He
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Learning From How Humans Correct
In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning
The Re-Label Method For Data-Centric Machine Learning
In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification
By aggregating distributed energy resources (DERs) into a single dispatchable resource, virtual power plants (VPPs) enable the grid to use the flexibility of numerous small-scale DERs. Delivering an aggregate VPP commitment requires multi-agent coordination, where agents manage local DER models and operating constraints while jointly meeting a power commitment within limited communication rounds. Classical distribute
Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeost
Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form, known as digital ink. However, a substantial gap remains between this way of note-taking and traditional pen-and-paper note-taking, a practice that is still favored by a vast majority. Our work InkSight, aims to bridge the gap by empowering physical note-takers to effortlessly conve
SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning
Human Activity Recognition (HAR) is a well-studied field with research dating back to the 1980s. Over time, HAR technologies have evolved significantly from manual feature extraction, rule-based algorithms, and simple machine learning models to powerful deep learning models, from one sensor type to a diverse array of sensing modalities. The scope has also expanded from recognising a limited set of activities to encom
Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
Controllable video generation has attracted significant attention, largely due to advances in video diffusion models. In domains such as autonomous driving, it is essential to develop highly accurate predictions for object motions. This paper tackles a crucial challenge of how to exert precise control over object motion for realistic video synthesis. To accomplish this, we 1) control object movements using bounding b
MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs
Generative large language models (LLMs) have been shown to exhibit harmful biases and stereotypes. While safety fine-tuning typically takes place in English, if at all, these models are being used by speakers of many different languages. There is existing evidence that the performance of these models is inconsistent across languages and that they discriminate based on demographic factors of the user. Motivated by thi
Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test
In this article, we introduce the novel concept of the second maximum of a Gaussian random field defined on a Riemannian manifold: Once the field reaches its global maximum at a point $t_1$, the second maximum is the largest value of the field away from $t_1$, after the value and the gradient of the field at $t_1$ have been removed. This statistic serves as a powerful tool for characterizing the distribution of the g
Pairwise Difference Learning for Classification
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training ex
UNLEARN Efficient Removal of Knowledge in Large Language Models
Given the prevalence of large language models (LLMs) and the prohibitive cost of training these models from scratch, dynamically forgetting specific knowledge e.g., private or proprietary, without retraining the model has become an important capability. This paper proposes a novel method to achieve this objective called UNLEARN. The approach builds upon subspace methods to identify and specifically target the removal
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typically computationally expensive and difficult to be deployed in resource-constrained environments. In this paper, we propose LLMR, a novel knowledge distillation (KD) method based on a reward function induced from large language models. We con
Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality
The Fréchet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estim
This paper addresses domain adaptation in graph-structured data under chronological splits. We consider transductive semi-supervised node classification (SSNC), in which labels of historical nodes are used to predict labels of more recent nodes. Temporal dependencies in graph connectivity induce distribution shifts across time, often degrading the performance of models trained on past nodes. Despite its practical rel
Text-to-Image Synthesis: A Decade Survey
Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanning the past decade, with representative advances covered through 2026. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation, and we additionally examine the shared components whose evolution has driven r
KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context
Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, w
Psychological evaluation relies on clinicians' observation and interpretation of a patient's emotional expression, which is susceptible to subjectivity, bias, fatigue, and inconsistency. This paper presents a multimodal emotion recognition system designed to support, rather than replace, the evaluator. The system combines facial expression recognition, speech emotion recognition, text-based emotion recognitio
BOLT: Universal Classifier Training via a Bayes-Error Upper Bound
This work introduces a novel approach to compute a universal upper bound on the Bayes error rate (BER) from classifier-output samples, which serves as a Bayes-error-related performance criterion for the underlying classification task. We show that the bound is tight in the binary case and characterize the max-sum gap that appears in the aggregate multiclass formulation. Based on this bound, we define a new loss funct
Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation
Pioneering companies such as Waymo have deployed electric robo-taxi services in several U.S. cities. Operating such fleets requires coordinating ride matching, vehicle repositioning, and battery charging in a stochastic environment while accounting for limited charging infrastructure. Understanding both how to design efficient, scalable control algorithms for large fleets and how charger deployment shapes system perf
FANTF: Fuzzy attention network based transformers for time series analysis
This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detection. FANTF combines fuzzy logic concepts with transformer architectures through a novel fuzzy attention mechanism. The proposed mechanism is inspired by Type-2 fuzzy set theory. It uses a learnable Gaussian uncertainty perturbation to model uncertainty in attention releva
A self-learning framework for adaptive free-form pipe routing in aeroengines
Pipe routing is a highly complex, time-consuming, and no-deterministic polynomial-time hard (NP-hard) problem in aeroengine design. Despite extensive research efforts in optimizing constant-curvature pipe routing, the growing demand for free-form pipes poses new challenges. Dynamic design environments and fuzzy layout rules further impact the optimization performance and efficiency. To tackle these challenges, this s
Generalising from Self-Produced Data: Model Training Beyond Human Constraints
Current large language models (LLMs) are constrained by human-derived training data and limited by a single level of abstraction that impedes definitive truth judgments. This paper introduces a novel framework in which AI models autonomously generate and validate new knowledge through direct interaction with their environment. Central to this approach is an unbounded, ungamable numeric reward - such as annexed disk s
Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard
Despite the success of Large Language Models (LLMs) in long-context tasks, their reliability in recognizing unanswerable questions remains a critical challenge. In information-seeking scenarios, models often suffer from overconfidence and hallucinations when evidence is missing or misleading. In this paper, we propose FACTGUARD, a systematic multi-task workflow for generating evidence-grounded answerable and unanswer
Learning in Structured Stackelberg Games
We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual information can be predictive of the follower's (unknown) type. Motivated by applications such as security games and AI safety, we show how this additional structure can help the leader learn a utility-maximizing policy in both the online and distributional settings. In the
Chronology of Multi-Agent Interactions for Provenance of Evolving Information
Provenance is the chronological history of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entities within the flow of space and time. As artificial intelligence advances towards autonomous agents capable of interactive collaboration on complex tasks, the provenance of generated content becomes entangled in the interplay of collective creation, where contributions ar
Origins publishing files written for machines rather than people. Measured against a frozen cohort, so a change in the number means a change in adoption.
| Signal | Web head Tranco top 1,000 n=1,000 | Web tail sampled to rank 100k n=1,000 | AI-native model & dev platforms n=110 |
|---|---|---|---|
| Any agent-facing signal | 12.6% (126) | 6.7% (67) | 73.64% (81) |
| llms.txt (apex domain) | 10.3% (103) | 5.7% (57) | 60% (66) |
| llms.txt (docs subdomain) | 4.2% (42) | 1.2% (12) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.2% (2) | 0% (0) |
| ai.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.7% (7) | 0.3% (3) | 0.91% (1) |
Notable events recorded on this day and month across all years.