Record 10092026 · captured 2026-09-11
The world looked up Ben Shelton. 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.
Benjamin Todd Shelton is an American professional tennis player. He has been ranked world No. 5 in men's singles by the Association of Tennis Professionals (ATP), achieved in November 2025. Shelton has won seven ATP Tour singles titles, including two Masters 1
Killing of the Clancy children
On January 24, 2023, Lindsay Clancy fatally strangled her three children—five-year-old Cora, three-year-old Dawson, and eight-month-old Callan—at the family's home in Duxbury, Massachusetts, United States. She then attempted suicide by cutting her wrists and n
Elizabeth Anne Holmes is an American businesswoman convicted of fraud in connection with her health technology company Theranos. Holmes founded Theranos in 2003, and its valuation soared in the early 2010s after it claimed to have revolutionized blood testing
Emma Navarro is an American professional tennis player. She has a career-high singles ranking of No. 8 by the WTA, achieved on September 9, 2024, and a doubles ranking of No. 93, achieved on August 12, 2024. Navarro has won three singles titles on the WTA Tour
Jessica Pegula is an American professional tennis player. She has a career-high rankings in singles of world No. 3, achieved in October 2022 and again in July 2026, and in doubles of world No. 1, achieved in September 2023. Pegula has won 11 singles titles and
The Navier–Stokes equations describe the motion of viscous fluids. This system of partial differential equations was named after Claude-Louis Navier and George Gabriel Stokes, who developed them over a few decades of progressive work, from 1822 (Navier) to 184
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
Trinity Rain Moyer-Rodman is an American professional soccer player who plays as a forward for the Washington Spirit of the National Women's Soccer League (NWSL) and the United States national team.
Hanuman Ansh is a 2026 Indian Hindi-language biographical devotional drama film written, directed, and produced by Dr Vishal Chaturvedi under his banner, Swambhu Media Network. The film is the first instalment of the film trilogy based on his book "Divine Deto
The September 11 attacks, colloquially known as 9/11, were a coordinated series of suicide attacks perpetrated by the Islamic terrorist organization al-Qaeda against the United States in 2001. A total of 19 terrorists hijacked four airliners, flying one into e
Mirzapur: The Movie is a 2026 Indian Hindi-language action crime thriller film directed by Gurmeet Singh and written by Puneet Krishna. Produced by Ritesh Sidhwani and Farhan Akhtar under Excel Entertainment, the film stars Pankaj Tripathi, Ali Fazal, Divyennd
Noel Anthony Clarke is an English former actor, writer, director, and producer. Rising to prominence for playing Mickey Smith in Doctor Who, he received critical acclaim for writing, directing, and starring in the teen crime drama films Kidulthood (2006), Adul
Navier–Stokes existence and smoothness
The question of whether the Navier–Stokes equations always have smooth solutions in three-dimensional Euclidean space, given some initial conditions, has been a longstanding unsolved problem in mathematics. A counter-example published in 2026 awaits scientific
Bryan Shelton is an American former professional tennis player and coach. During his playing career, he won two singles and two doubles ATP tour titles, and reached the mixed doubles final at the 1992 French Open, partnering with Lori McNeil. Shelton played co
Carlos Alcaraz Garfia is a Spanish professional tennis player. He has been ranked world No. 1 in men's singles by the Association of Tennis Professionals (ATP) for 66 weeks, and finished as the year-end No. 1 in 2022 and 2025. Alcaraz has won 26 ATP Tour–level
You Can See Everything is a 2026 American documentary film directed by Nathan Fielder and Lance Oppenheim. It follows Elizabeth Holmes, the founder of the defunct health technology company Theranos, and her partner Billy Evans who invite a film crew to documen
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
Nathan Joseph Fielder is a Canadian filmmaker, actor, and comedian, known for his awkward persona and for creating works that blur the line between reality and fiction.
Frances Tiafoe Jr. is an American professional tennis player. He has a career-high singles ranking of world No. 10, achieved in June 2023, and a best doubles ranking of No. 160, reached in November 2021. Tiafoe has won four ATP Tour singles titles across all t
The Gentlemen (2024 TV series)
The Gentlemen is a black comedy crime drama television series created by Guy Ritchie for Netflix. It is a spin-off of Ritchie's 2019 film of the same name, taking place in its fictional universe and sharing thematic connections but following a standalone story
Elena Andreyevna Rybakina is a Russian-born Kazakhstani professional tennis player. Currently world No. 2 in women's singles, she has secured the No. 1 spot in the ranking to be released on 14 September 2026. Rybakina has won 13 WTA Tour-level singles titles,
.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
Benjamin W. Navarro is an American billionaire businessman who is the founder and chief executive officer of Beemok Capital, a private investment firm and family office. Through Beemok, he oversees a range of initiatives, including Beemok Hospitality Collectio
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian psychological thriller gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations respectively. It stars Yash, Kiara Advani,
Remain is an upcoming American supernatural romantic thriller film written, directed, and produced by M. Night Shyamalan, based on an idea he developed with Nicholas Sparks, who separately wrote a version of the story as a novel published in 2025. The film sta
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
Mirra Aleksandrovna Andreeva is a Russian professional tennis player. She has been ranked by the WTA as high as world No. 5 in singles, achieved in July 2025, and No. 12 in doubles, achieved in September 2025. Andreeva has won six WTA Tour–level singles titles
Charles James Kirk was an American right-wing political activist, entrepreneur, and media personality. He co‑founded the conservative student organization Turning Point USA (TPUSA) in 2012 and served as its executive director until his assassination in 2025. A
The Millennium Prize Problems are seven well-known complex mathematical problems selected by the Clay Mathematics Institute in 2000. The Clay Institute has pledged to pay one million US dollars for the first correct solution to each problem.
Cori Dionne "Coco" Gauff is an American professional tennis player. She has a career-high ranking of world No. 2 in singles and of world No. 1 in doubles by the WTA. Gauff has won twelve career singles titles, including two majors at the 2023 US Open and 2025
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter steps issued through a generic tree interface. On a GPU every kernel finishes in microseconds, so kernel launches and framework dispatch dominate the run time, and prior
General Quantification of Covariate and Concept Shifts
Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples. In this paper, we bridge the gap between theory and practical applications. We first show that existing definition of concept shift breaks when the source and target supports mismatch. Leveraging entropic optimal tra
Can Edge-Deployable Vision-Language Models Identify Species?
Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification. We test whether models in this deployment-relevant 2--8B range carry genuine taxonomic knowledge, evaluating four such VLMs (Qwen3-VL 2B/4B/8B, Gemma3 4B) against the dom
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares
Artificial Id: Drive and Persistent Alignment in Agentic AI
Agentic AI is moving from bounded task execution toward systems that retain consequential state, continue operating and adapt across task boundaries. That shift creates a control problem that current harnesses largely solve by hand: objectives, retries, verification, stopping rules and other behavioral transitions are specified externally. We propose an artificial id, an adaptive internal drive for determining whethe
MindTopo: Can Foundation Models Reason in Topological Space?
Spatial reasoning depends not only on metric properties such as distance, angle, and shape, but also on topological relations that remain invariant under continuous deformation. Cognitive science identifies these relations as foundational to spatial understanding, yet foundation-model evaluations largely focus on metric or viewpoint-dependent relations. We introduce MindTopo, a benchmark of topological intuition acro
Domain-Specific Hallucination Detection in Large Language Models
Large language models generate fluent text that can contain unfaithful claims -- a phenomenon known as hallucination. We present a multi-signal detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo (MC) Dropout uncertainty quantification, and temperature-scaled calibration for response-level hallucination detection. Evaluated on the HaluEval benchmark, our pipeline achieves F1=0.915 and AUROC
Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens
Many biological discovery problems require experiments to be selected sequentially under constrained budgets. CRISPR screening is a prominent example, as exhaustive perturbation testing is often infeasible and candidate perturbations must instead be prioritized over multiple experimental rounds. Despite the importance of this problem, existing benchmarks for adaptive hit discovery remain limited in scale and diversit
On the Regularization Landscape for the Linear Recommendation Models
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders on several standard recommendation benchmarks. While these algorithms were built on different DL techniques (e.g., dropouts, autoencoder), they have similar performance and even similar cost functions. This paper studies whether the models' comparable performance are sheer coincidence, or t
The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition
RetroThinker: Enabling Retrospective Thinking in Speech LLMs
Speech large language models (SpeechLLMs) offer reduced latency and retain paralinguistic nuances that are typically lost in cascaded automatic speech recognition (ASR) and text-based LM architectures. However, they continue to lag behind text-only LLMs on complex reasoning tasks, while real-time spoken interaction imposes strict latency constraints. Although prior works employ Chain-of-Thought (CoT) and concurrent r
Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models
Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, whose predictions can be difficult for facility managers and building operators to interpret. Explainable Artificial Intelligence (XAI) techniques address this opacity, but traditional XAI dashboards require substantial technical expertise and provide limited flexibility f
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on the hidden state at the end of the question. Across Qwen, Llama, and Gemma, we compare country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct. A pair-conditioned request directi
Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport
Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kinetic action defined on coefficient paths, motivated by optimal transport, helps explain strong baselines but remains model-agnostic and ignores prediction error. Here we introduce a model-aware schedule construction based on fiberwise optimal transport. At a fixed tim
Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations
Maritime Autonomous Surface Ships (MASS) and AI- supported decision assistants are expected to transform maritime operations, but their safe integration depends on how maritime professionals perceive and trust such systems. This paper presents a survey study on maritime stakeholders' attitudes toward an AI-supported assistant in collision-avoidance scenarios. Participants evaluated technology anxiety, trust in automa
Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling
Visual Autoregressive Models (VAR) generate images through next-scale prediction, producing all tokens within each scale in parallel. We show that this parallel decoding constitutes a mean-field-style approximation that discards spatial dependencies among same-scale tokens, causing locally incoherent samples regardless of backbone capacity -- a limitation of the decoding rule. Addressing this limitation, we introduce
Humans and machines often solve harder problems by spending more time on computation. In deep learning, looped models implement this idea during inference by recurrently updating a hidden state. In practice, however, their training backpropagates through only one or a few updates, making it hard to train early updates to support future ones. We propose looped flows, an approach that sidesteps this issue by training t
Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poorly characterized. We present a switch aware evaluation of eleven modern systems (six ASR models and five audio LMs) on English Yoruba code-switched speech, using a deterministic 2000 utter
Recognizing Is Not Reversing: A Controlled Inversion Test of Fact-Preserving News Framing
Large language models (LLMs) are increasingly used to analyze and rewrite news, yet current framing studies mainly evaluate generation, detection, or whether rewritten text appears more neutral. They do not directly show whether a model can undo a known framing transformation while keeping the facts fixed. We introduce a controlled inversion test over three established textual realizations of framing: evaluative lexi
Per-token gating of forward/reverse KL losses has become a standard technique for on-policy knowledge distillation (OPD), but existing methods such as EOPD (Jin et al., 2026) and ToDi (Jung et al., 2025) each fix a single gating signal and a single gating direction, and the two have never been compared directly. We introduce a four-coefficient parameterization lambda_t = sigma(a * h_t + b * u(x) + c + d * gap_t) in w
SIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control
For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level latency. We present SIRF (Spec-Internalized Risk Foundation Model), which internalizes a platform's complex policies, synthesized without additional human annotation via EntiGraph, MAGA rewriting and account-level chain-of-thought (CoT), into the weigh
LOCUS: Task-Aware Low-Rank Post-Training for Token-Efficient Language Generation
Large language model serving costs scale directly with output sequence length, yet standard preference alignment often inflates response verbosity without improving utility. We study whether the parameterization of post-training updates affects generation length: low-rank subspaces alter sequence length without modifying the alignment loss. We present LOCUS, a method that selects a task-aware low-rank adaptation subs
ORCH: Organizational Principles Enable Collective Intelligence in Embodied AI
Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artificial multi-agent systems are typically assembled using fixed organizational structures, even when the physical tasks they perform impose fundamentally different coordination requirements. Here we show that principles from human organization theory can be operationalized to organiz
Continuous-Time Acoustic Modelling with Neural Controlled Differential Equations
Text-to-speech (TTS) models commonly address text--speech alignment by expanding phone-level encoder states to frame-level decoder inputs using predicted durations. While this length-regulation step resolves alignment structurally, this use of duration typically changes only where and how often latent states appear, not the values of the states themselves. This paper proposes a continuous-time mechanism for duration-
A Time-Based Readout for Vector-Matrix Multiplication in Fully Analog Memristive SNNs
Artificial neural networks rely on vector-matrix multiplications (VMMs), whose implementation in von Neumann architectures is dominated by costly data movement between memory and processing units. Spiking neural networks (SNNs) mitigate this bottleneck by performing in-memory, analog VMMs using memristive crossbar arrays. However, conventional current-mode readout circuits incur significant area and power overhead. T
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 | 11.3% (113) | 3.9% (39) | 74.55% (82) |
| llms.txt (apex domain) | 9.1% (91) | 3.2% (32) | 60% (66) |
| llms.txt (docs subdomain) | 3.4% (34) | 1% (10) | 44.55% (49) |
| .well-known/mcp.json | 0.6% (6) | 0.1% (1) | 7.27% (8) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 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.