Record 09092026 · captured 2026-09-10
The world looked up Elizabeth Holmes. 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.
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
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
Alex T. Michelsen is an American professional tennis player from California. His parents, Sondra and Erik Michelsen, both played tennis in college. He has a career-high ATP singles ranking of world No. 30 achieved on July 14, 2025 and a best doubles ranking of
Bernard Boursicot is a French diplomat who was caught in a Chinese honeypot trap by Shi Pei Pu, a male Peking opera singer who performed female roles, whom Boursicot claimed he believed to be female. This espionage case became something of a cause célèbre in F
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 Detour
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
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, who invited a film crew to document her in the weeks before s
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
Alice Elisabeth Weidel is a German politician who has been serving as co-chairwoman of the far-right Alternative for Germany (AfD) party alongside Tino Chrupalla since June 2022. Since October 2017, she has held the position of leader of the AfD parliamentary
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 Falling Man is a photograph taken by Associated Press photographer Richard Drew of an unidentified man falling from the World Trade Center during the September 11 attacks in New York City, United States. The unidentified man in the image was trapped on the
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
Alternative for Germany is a far-right, right-wing populist, national conservative, and in parts völkisch nationalist political party in Germany. It has 151 members of the Bundestag and 15 members of the European Parliament. It is the largest opposition party
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
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
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,
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 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 planned and subsequently hijacked four air
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
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, is or was a longstanding unsolved problem in mathematics. A proposed solution published in September 2026 awai
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
Gopalaswamy Doraiswamy Naidu was an Indian innovator, inventor, industrialist, and educator. He redesigned and transformed imported technologies into practical and affordable innovations for India, and is widely regarded as a versatile genius. His contribution
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
Coyote vs. Acme is a 2026 American comedy film directed by Dave Green and written by Samy Burch, who developed the story with James Gunn and Jeremy Slater. Loosely based on the 1990 The New Yorker magazine article "Coyote v. Acme" by Ian Frazier, it follows Wi
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Neem Karoli Baba also known as Neeb Karori Baba and by his followers as Maharaj-ji, was a Hindu guru and devotee of the Hindu deity Hanuman.
Iva Jovic is an American professional tennis player. She has a career-high WTA singles ranking of No. 14, achieved on August 24, 2026, and a best doubles ranking of No. 64, reached on the same date. She has won one WTA Tour title, at the 2025 Guadalajara Open.
Aryna Siarhiejeŭna Sabalenka is a Belarusian professional tennis player. She is the current world No. 1 in women's singles by the WTA and is a former No. 1 in doubles. Sabalenka has won 24 career singles titles, including four majors—two each at the Australian
.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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Show-Harness: Just a VLM Agent Can Play Robots
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific int
IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier
Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet all 18 audited benchmarks score advertised model identifiers. We treat this as measurement error and give a protocol that makes it reportable. It has three parts. A gold-blind capability-binding preflight verifies that a route can execute the evaluation contract before
Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization
Joint-Embedding Predictive Architecture (JEPA) world models learn a compact latent representation of the world that supports prediction and planning, but their capability to learn physics and generate physically realistic dynamics remains hitherto untested. In this work, we introduce SemiGroup-JEPA (SG-JEPA), which extends the LeWorldModel framework by supplying the parameter governing the physics to the temporal mod
JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition
Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly opt
ConvMem: Convolutional Memory for Long-Context Reasoning
While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory. However, this sequential paradigm suffers from high latency and requires costly reinforcement learning (RL)
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machine unlearning offers a practical alternative to full retraining, but many existing methods apply broad or fixed parameter updates that can degrade utility and remain brittle under deployment changes such as post-training quantization, where
Background: Emergency Department (ED) return visits are commonly reviewed for quality assurance, but are often limited (e.g., to revisits within 48-72 hours) to increase actionable finding yield while minimizing chart review burden. Those limitations may lead to missed quality improvement opportunities. Methods: We conducted an exploratory, retrospective study of randomly selected ED visits to a multihospital health
Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs
Current LLM memory systems treat all personal facts identically, so stores grow without bound while retrieval precision degrades. The core challenge is lifecycle management: which memories should persist, which should be replaced, and at what rate, conditioned on the behavioral type of each fact. Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and appl
The growing complexity of content moderation policies presents a critical challenge for their consistent operationalization. While foundation models possess the basic capabilities needed to confront this challenge, whether they can reliably moderate online content remains an unanswered question. In this paper, we systematically compare two competing paradigms for Vision-Language Model (VLM) guidance: an instruction-d
Animation and VFX pre-production review requires teams to translate loosely specified creative intent--briefs, evolving specifications, heterogeneous references, and verbal decisions--into revisions that junior artists can execute without repeated clarification. In practice, criteria drift across iterations, review judgments lose their evidential basis, and the reasoning behind a request rarely survives the senior-ju
We present the PACE, a framework for retrieval-augmented dialogue serving that formalizes Perceived Time-to-First-Response (PTFR) as a QoE objective and minimizes it under quality/cost constraints. Unlike prior work on cascaded routing, semantic caching, or adaptive retrieval, PACE jointly controls which answer source composes the response and what fills the waiting window. Deployed on a humanoid-robot sales service,
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial void of secure fusion of visual and textual context across distant networks. Thus, we present OmniM
The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering triage, customer analytics, and internal decision support. This paper studies how a compromise inside one of those vendors can propagate along a chain of operational, informational, and financial linkages until it triggers losses that look, from the outside, like a cla
Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs
Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing vision token pruning methods mitigate this overhead, they implicitly assume that a single fixed pruning strategy can be applied uniformly across all inputs. Our analysis further reveals that ranking pruning methods by average benchmark accuracy conceals substantial s
Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally handle visuo-linguistic inputs, they are often computationally intensive and opaque. We demonstrate that a pure Large Language Model (LLM), when equipped with specialized modules, can rival state-of-the-art LMMs on complex geometry problems. Ou
TRACE: Training Reasoning Agents for Causal Exploration with Synthesized Rewards
Reinforcement learning with verifiable rewards (RLVR) has advanced language-model reasoning in domains such as mathematics and code, where objective answers are inexpensive to check. Diagnostic reasoning over complex data lacks this advantage: establishing the true cause of an anomaly often requires costly expert investigation and may remain ambiguous after the fact. We ask whether this asymmetry of verification can
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initialization, match the accuracy of the full dense network. The predominant method for discovering such tickets, iterative magnitude pruning, alternates pruning with full retraining from scratch until convergence over many cycles. Similarly, deep active learning also retrains
RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding
Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transformer at embedding width d = 128 still spends roughly one third of its capacity on the output matrix W_out in R^(d x |V|). We propose Riemannian Language Models (RiLM), which remove that layer entirely: context unfolds as a trajectory on a Riemannian manifold, and next-t
Learning Intrusion Response Strategies for OT Systems
Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential societal services. For this reason, developing automated intrusion response strategies is highly important. In this paper, we present a formal model of an OT intrusion response use case using the POMDP framework. It includes a realistic model of partial observability that is
GANDR: Claim Auditing for Verifiable Legal Answer Generation
In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the source the system cites. Current grounded-generation pipelines score the answer as a whole, so a correct conclusion can rest on fabricated or loosely matched citations and still score well. Closing this gap requires both a system built for per-claim verification and an ev
What Should an Agent Forget? Separating What Is Stored from What Is Used
Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question. A superseded fact can mislead a current-state answer and still be essential for a historical query. We present RD-Forget, a training-free framework that separates what an agent stores from what it uses. A retained source archive preserves observations, and a query-con
Large language models (LLMs) are increasingly deployed in globally used assistants, yet their default choices in culturally grounded everyday situations can systematically favour some cultures over others, affecting localisation, user trust, and equitable behaviour. Existing cultural benchmarks evaluate accuracy against a single "correct" answer, making it difficult to characterise an LLM's cultural preference prior
A-JIT: Agentic Just-In-Time Software Construction
Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic Just-In-Time Software Construction (A-JIT), a paradigm that replaces static binaries with dynamic, software systems that can perpetually evolve to meet changing demands. In A-JIT, an application is an integrated assembly comprising code, a runtime harness, and an embedde
LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversized contexts that reduce generation efficiency. We present LiteRAG, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction. On DistComp, a benchmark for mul
Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformation knowledge into continuous embedding spaces for efficient exploration of candidate strategies, but face three key limitations: (1) overlooking hierarchical relationships between low-level features, operations, and high-level abstractions;
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 | 10.9% (109) | 4.1% (41) | 75.45% (83) |
| llms.txt (apex domain) | 8.7% (87) | 3.4% (34) | 60.91% (67) |
| llms.txt (docs subdomain) | 3.4% (34) | 1% (10) | 43.64% (48) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 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.