Record 29092026 · captured 2026-09-30
The world looked up Pac (wrestler). 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.
Benjamin Satterley, better known by his ring name Pac, was an English professional wrestler. He was best known for his time in All Elite Wrestling (AEW), where he was a member of the Death Riders and Death Triangle, and WWE, where he previously performed under
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.
Theodore John Kaczynski, also known as the Unabomber, was an American mathematician and domestic terrorist. A mathematics prodigy, he abandoned his academic career in 1969 to pursue a reclusive primitive lifestyle and lone wolf terrorism campaign. Kaczynski mu
The 2026 Asian Games, officially known as the XX Asian Games or Aichi–Nagoya 2026, is the largest sporting event in Asia governed by Olympic Council of Asia (OCA). They are currently being held from 19 September to 4 October 2026 in Aichi Prefecture and Nagoya
India is participating in the 2026 Asian Games in Aichi Prefecture, Japan from 19 September to 4 October 2026. This is the country's 20th consecutive appearance in the competition, having made its debut at the inaugural 1951 Asian Games. The Indian contingent
Katherine Elizabeth Upton is an American model and actress. She first appeared in the Sports Illustrated Swimsuit Issue in 2011, and was the cover model for the 2012, 2013, and 2017 issues. In addition, she was the subject of the 100th-anniversary Vanity Fair
Christa Gail Pike is an American convicted murderer who is incarcerated on death row in Tennessee. In 1995, when she was 18, Pike murdered her 19-year-old Job Corps classmate Colleen Slemmer in Knoxville. Pike and two friends led Slemmer into the woods, where
Justin Brooks Verlander is an American former professional baseball pitcher who played 21 seasons of Major League Baseball (MLB). He played for the Detroit Tigers, Houston Astros, New York Mets, and San Francisco Giants. A three-time Cy Young Award winner, an
The 2026 MTV Video Music Awards were held at the Peacock Theater in Los Angeles, California, on September 27, 2026. It was broadcast live on CBS, simulcast on MTV, and streamed on Paramount+'s premium tier. The show was hosted by rapper Snoop Dogg.
The 2026 Asian Games , officially the XX Asian Games (第二十回アジア競技大会) and also known as Aichi–Nagoya 2026 (愛知・名古屋2026), is an ongoing multi-sport event being held in Aichi Prefecture in Japan from 19 September to 4 October 2026 though several competitions started
Madonna Louise Ciccone is an American singer, songwriter, record producer, and actress. Dubbed the "Queen of Pop", she is known for her continual reinvention and versatility in music production, songwriting, and visual presentation. Her works, which concern so
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
Lanterns is an American superhero television series created by Chris Mundy, Damon Lindelof, and Tom King for HBO, based on the DC Comics Green Lantern characters Hal Jordan and John Stewart. It is the third television series in the DC Universe (DCU). It featur
The Paradise (2026 Indian film)
The Paradise is a 2026 Indian Telugu-language period action drama film directed by Srikanth Odela and produced by Sudhakar Cherukuri and Ishan Saksena, under Sri Lakshmi Venkateshwara Cinemas and IVY Entertainment, respectively. The film stars Nani, Mohan Babu
Aileen Carol Wuornos was an American serial killer. Between 1989 and 1990, while engaging in street prostitution along highways in Florida, Wuornos shot, killed, and robbed seven of her male clients. She initially claimed that her victims had either raped or a
A hunger stone is a type of hydrological landmark common in Central Europe. Hunger stones serve as famine memorials and warnings and were erected in Germany and in ethnic German settlements throughout Europe in the 15th through 19th centuries.
George Michael was an English singer-songwriter and record producer. Regarded as a pop culture icon, he is one of the best-selling recording artists of all time. Michael has been widely recognised as a creative force in songwriting, vocal performance, and visu
Hasmah binti Mohamad Ali, better known as Siti Hasmah, was a Malaysian physician and civil servant. As the wife of Mahathir Mohamad, the fourth and seventh prime minister of Malaysia, she served as the spouse of the prime minister of Malaysia from 1981 to 2003
Primetime is a 2026 American psychological crime thriller film directed by Lance Oppenheim in his narrative feature film debut. Based on the 2007 Esquire article "Tonight on Dateline This Man Will Die" by Luke Dittrich, the film follows the production of the r
Jonathan James McCarthy is an American professional football quarterback for the New York Giants of the National Football League (NFL). He played college football for the Michigan Wolverines, earning Big Ten Quarterback of the Year honors after leading the Wol
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
Starship flight 14 was the fourteenth launch and the first operational flight of the SpaceX Starship launch system. Building on the preceding flight-test campaign, the mission marked a significant expansion of Starship's objectives, successfully entering a sus
The Loyal Orange Institution, commonly known as the Orange Order, is an international Protestant fraternal order based in Northern Ireland and primarily associated with sectarianism and Ulster Protestants. Due to the spread and outreach of Orangeism, there are
Monster: The Lizzie Borden Story
Monster: The Lizzie Borden Story is the fourth season of the American biographical crime drama anthology television series Monster, created by Ian Brennan for Netflix. The season centers on murder suspect Lizzie Borden who was tried and acquitted of the 1892 a
Resident Evil is a 2026 horror film directed by Zach Cregger and written by Cregger and Shay Hatten, based on the Resident Evil video game franchise by Capcom. It is the eighth live-action film of the Resident Evil film series, and serves as a reboot. The film
Lalisa Manobal, known mononymously as Lisa, is a Thai rapper, singer, dancer, and actress. She rose to prominence as a member of the South Korean girl group Blackpink, which debuted under YG Entertainment in August 2016 and became one of the best-selling girl
Dorothy is a 2026 Indian Tamil-language drama film directed by Karthik Subbaraj and produced by Guneet Monga Kapoor, Achin Jain and Jyoti Deshpande. The film stars Keerthy Suresh, Sananth and Rishikanth. It follows two inseparable friends whose lives are trans
Nirvana was an American rock band formed in Aberdeen, Washington, in 1987. Founded by lead singer and guitarist Kurt Cobain and bassist Krist Novoselic, the band went through a succession of drummers, most notably Chad Channing, before recruiting Dave Grohl in
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
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'.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Skill-Space Shooting for Autonomous Robot Policy Improvement
Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this improvement to scale across tasks, it must make effective use of experience without requiring human demonstration of each correction. Recent agentic systems offer a way to reduce this reliance on human effort by using foundation models to autonomously compose learned beha
STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization
Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states can become a substantial memory bottleneck under concurrent serving. Directly quantizing recurrent states to low precision often leads to severe accuracy degradation, as quantization errors propagate through successive state updates. We discover that the impact of these errors depends on two complementary dimensi
LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA). Although they compress the context into a fixed-size recurrent state and substantially reduce the cost of long-context processing, repeatedly reading and updating that state remains a major inference bottleneck. Quantization offers a natural way to reduce th
Beyond the Timeline: Augmenting Long-Video Memory with Grounded Entity Biographies
Answering questions about long videos often requires connecting events involving the same objects across hours or days. Chronological descriptions and text-derived entities can leave physical identity unresolved: different objects may share a description, while observations of the same object remain disconnected across events. Retrieving relevant events therefore does not necessarily recover the "biography" of the pa
Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control choices, like which partial work to build on, whether to start fresh, or when to stop. We introduce agentic meta-reasoning, an inference-time harness that makes these choices an explicit and structured reasoning process. Workers carry out the task-level computation, wh
Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI
Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn to construct better execution environments for a Target while both models' weights remain fixed. To make the Builder's experience reusable, we introduce Meta-Skill: principles specifying when support is needed and what resources to provide. The Builder learns these pri
AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation
A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advisor can use feedback from completed interactions to improve its advice. However, a plausible correction need not change execution, yet learning from such corrections can still affect the advisor's future decisions in other contexts. In a shared-parameter model, we prov
Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE
Mixture-of-Experts (MoE), popularized by large language models, is a promising paradigm for scaling visual generative models. However, conventional token-wise MoE routes tokens independently within a homogeneous expert pool and regularizes expert usage toward uniformity, making it poorly matched to video data that is spatiotemporally redundant and semantically long-tailed. We show that existing visual MoEs fall into
Stochastic World Models for Verifying Vision-Based Neural Feedback Systems
Verifying a vision-based neural feedback system requires a model of the observations its controller acts upon. Such a model must capture the variation the sensor produces, while remaining tractable for closed-loop analysis. Generative adversarial networks (GANs) have served as perception surrogates, but they are large, reproduce complex scenes poorly, and are hard to verify. We explore stochastic world models as a ri
How Local Mixing Encodes Relative Position in Global NoPE Attention
The attention operation is naively position invariant. However, positional information is fundamental to natural language, and therefore a variety of explicit position encodings have been developed in transformer-based models, such as rotary position encoding (RoPE). Although explicit position encodings have long been assumed to be required, recent methods that interleave local mixing layers, such as sliding window a
Large language models (LLMs) enable agents to solve long-horizon tasks by generating a plan and then executing it in an environment. However, successful planning requires two distinct capabilities: selecting an appropriate plan for the task and executing it faithfully. Existing planner--executor systems can fail at either stage, while final task success alone cannot distinguish selection from execution failures. We t
Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interpretation is difficult because natural-language thinking traces are rarely mechanically verifiable. We revisit it in iGSM, a synthetic grade-school mathematics benchmark designed to study thinking traces and used to support claims of learned reasoning
NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning
Current vision-language models (VLMs) encode visual information in dense hidden states where object identity, spatial layout, and local attributes are implicitly entangled rather than explicitly disentangled, limiting their ability to isolate and modulate the specific visual evidence required by a given language query. Inspired by sparse population coding and top-down modulation in biological vision, we introduce Neu
Character Training for Risk-Averse Agents
Risk aversion in resources could prevent misaligned AI agents from causing catastrophic harm. Misaligned but risk-averse agents would tend to favor safer strategies like making deals with humans over riskier strategies like rebelling. We train agents to be risk averse through character training, finding that persona traits provide a robust mechanism for instilling risk preferences. To do this, we construct a model co
Neural topology optimization of ship structures under propulsion machinery vibrations
Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance. This study extends neural-reparameterized topology optimization using a convolutional Kolmogorov-Arnold network (KATO) to forced-vibration design with active input power (AIP) as the objective. Applications include a 100 Hz engine-supporting dec
As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important for quantifying the uncertainty of their answers. Current methods for estimating the confidence of large language models are generally based on probabilities of selected key tokens, but the underlying mechanism remains unclear. Our pilot study finds
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and structured formalism for specifying instructions to agents, and has been successfully adopted for training generalist multi-task policies. However, differences in implementations, task distributions, and evaluation protocols make existing methods difficult
UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training
Interactive agent benchmarks and multi-turn reinforcement learning increasingly place a second language model in the role of the user. This simulated user controls what information the agent receives and when, yet current benchmarks score only the agent and do not directly measure whether the user correctly executed its assigned role. We introduce UserProxyBench, an evaluation layer over the tau-bench family, and the
Gender bias across LLMs is common and highly heterogenous
Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with real consequences. Prior research has focused only on a small set of models, leaving open the extent to which gender biases are common and heterogeneous across LLMs. We address this gap across ten models released between April 2025 and June 2026, spanning nine vendors,
Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, depend on future events, refer to surrounding agents or landmarks, and remain relevant as driving conditions evolve. Existing language-enabled driving datasets largely focus on short, localized interactions, leaving these longer-horizon forms of passeng
Dr. OPD: Learning What to Follow for Optimal On-Policy Distillation of Large Language Models
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD treats all teacher signals equally, assuming that the teacher's supervision is equally important for every token. However, teacher signals at different tokens may have very different effects on the student's performance: some correct important reasoning errors, while o
Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation
An agent skill is a reusable, actionable natural-language artifact that guides an agent to perform a task effectively under a given harness. Recent studies have explored the optimization of agent skills, contributing to a growing collection of publicly available skills spanning diverse tasks, domains, and harnesses. Despite millions of publicly shared skills, existing skill optimization methods largely overlook this
PE-EK-PINN: Physics Embedding with Evolving Kernel for Scalable Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) embed governing equations into deep learning, but enforce them only through loss residuals, leaving highly oscillatory wave behavior to be discovered by optimization. As a result, methods that achieve relative $L_2$ errors below $10^{-3}$ on standard manufactured Helmholtz benchmarks can fail on practical radiation problems involving singular excitations, absorbing boundaries,
We evaluate an auditable long-term memory system on LongMemEval-S. Its retrieval chain uses hybrid candidate retrieval, cross-encoder reranking, coverage-first packet compilation, and deterministic reasoning scaffolds; an LLM is used only as a replaceable final reader. The chain places all gold sessions in the candidate pool for 468/470 answerable questions and produces gold-complete packets for 462/470. With a Claud
Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to maximize the expected reward while keeping the overall action risk bounded. In this way, the safety issues arising in AD can be mitigated through constrained actions. However, existing Constrained RL methods still lack dynamics on the imposed constraints. For instance,
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 | 12.1% (121) | 5.2% (52) | 74.55% (82) |
| llms.txt (apex domain) | 9.9% (99) | 4.4% (44) | 60% (66) |
| llms.txt (docs subdomain) | 4.1% (41) | 1% (10) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 8.18% (9) |
| .well-known/agents.txt | 0.3% (3) | 0.2% (2) | 0% (0) |
| ai.txt | 0.2% (2) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.1% (1) | 0.91% (1) |
Notable events recorded on this day and month across all years.