Record 28092026 · captured 2026-09-29
The world looked up Lizzie Borden. 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.
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.
Justin Brooks Verlander is a 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 A
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
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
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 which the public is exited at the c
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
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
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
The 2026 All Out was a professional wrestling pay-per-view (PPV) event produced by the American promotion All Elite Wrestling (AEW). It was the eighth annual All Out and took place on September 26, 2026, at the Now Arena in Hoffman Estates, Illinois. This was
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
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
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
Virat Kohli is an Indian international cricketer and former all-format captain of the Indian national cricket team. He is a right-handed batter and an occasional right-arm medium-pace bowler. Considered one of the greatest batters in cricket, he has been accla
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
Katseye is a girl group based in Los Angeles. The group consists of six members: Daniela Avanzini, Lara Raj, Manon Bannerman, Megan Skiendiel, Sophia Laforteza, and Yoonchae Jeung. With members from the Philippines, South Korea, Switzerland, and the United Sta
The Loyal Orange Institution, commonly known as the Orange Order, is an international Protestant fraternal order based in Northern Ireland and primarily associated with Ulster Protestants. Due to the spread and outreach of Orangeism, there are lodges around th
Backrooms is a 2026 American psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture store owner, and h
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
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
Heart of the Beast is a 2026 American survival thriller film directed by David Ayer and written by Cameron Alexander. The film stars Brad Pitt as a retired Special Forces veteran navigating a grueling 58-mile trek to safety with his traumatized combat German S
Unabomber is a 2026 American crime drama television film directed by Janus Metz Pedersen and written by Sam Chalsen and Nelson Greaves based on the domestic terrorist, Ted Kaczynski. The film stars Russell Crowe, Jacob Tremblay, Annabelle Wallis, and Shailene
Avengers: Endgame is a 2019 American superhero film based on the Marvel Comics superhero team the Avengers. Produced by Marvel Studios and distributed by Walt Disney Studios Motion Pictures, it is the direct sequel to Avengers: Infinity War (2018) and the 22nd
The Drumcree conflict or Drumcree standoff is a dispute over yearly parades in the town of Portadown in the north of County Armagh, Northern Ireland. The town is mainly Protestant and hosts numerous Protestant and Ulster loyalist marches each summer, but has a
Thomas Jonathan Bateman is a British actor best known for his roles as Giuliano de' Medici in the Starz historical fantasy drama series Da Vinci's Demons (2013–2015), as Bouc in the mystery films Murder on the Orient Express (2017) and Death on the Nile (2022)
Christopher Edward Hansen is an American television presenter, journalist, and YouTube personality. During his tenure as a correspondent for Dateline NBC, he hosted the program's segment To Catch a Predator (2004–2007), which revolved around catching potential
The passenger pigeon or wild pigeon is an extinct species of pigeon that was endemic to North America. Its common name is derived from the French word passager, meaning "passing by", due to the migratory habits of the species. The scientific name also refers t
.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
Forgotten Island is a 2026 American animated fantasy comedy film written and directed by Joel Crawford and Januel Mercado. Produced by DreamWorks Animation, the film features the voices of H.E.R., Liza Soberano, Dave Franco, Jenny Slate, Manny Jacinto, Dolly d
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self-occlusion and obfuscation, and, unlike human hair, the lack of animal-fur datasets. Fur usually covers most of an animal's body, with large inter-species and intra-species variability. We present FurE, an efficient strand-based animal fur reconstruction method that recovers a per-strand, editable groo
One deployed language model must often serve many compute budgets, yet serving each budget still means a separate training or compression run per point. We train a Telescopic Language Model (TLM) to be that continuum: a nested-capacity Transformer supervised by stochastic prefix supervision with a full anchor. At every step, one randomly truncated prefix of the capacity axis is trained against the full next-token tar
Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning
Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again. Whether a revision helps is known only after it is rendered, so the reflection text and the image generation must be learned jointly, over the whole loop. Supervised fine-tuning (SFT) on reflection trajectories gives
TokenCast: Forecasting Token Consumption During LLM Agent Execution
When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next steps based on tool feedback and intermediate results, while the growing context steadily inflates the input size of every subsequent call. The total consumption of a task is therefore hard to predict before execution and the prediction must be revised as the
How to Loop MoE: Flatten the Experts, Untie the Attention
Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its parameters more fully; while sparse mixture-of-experts (MoE) models activate only a few of many experts for each token. Looped MoE bridges these two design philosophies and gives MoE models new potential for better expert usage, but it raises a question: how to loop a MoE?
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate this issue, keeping GPU memory constant for a given trace. Unfortunately, most compaction strategies rely on prefilling the LLM context many times over, hindering training throughput. To alleviate this bottleneck and enable efficient train
Copy the Same, Distill the Difference: Initializing Linear Vision Transformers
Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient token routing, but they require from-scratch pre-training and typically underperform the original Softmax version. How to initialize linear ViTs both efficiently and effectively still remains unclear. In this work, we explicitly ask: given that most foundation ViTs a
FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents
Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric, experts specify reusable evaluation guidance, while agents and code carry out query-specific rubric generation, review
Shockingly Simple Self-retrospection Improves Agentic Models Without RL
People learn not only by repeating successful actions, but also by recounting and explaining their experiences, revising their understanding to guide future behavior. Can a language-model agent improve its future actions by training only on explanations of its own experience? We investigate this question by studying Retrospection-Only Fine-Tuning (ROFT), a minimal online procedure designed to isolate the effect of ex
Failure-Transparent Agents: Benchmarking Post-Failure Reporting in Tool-Using Language Models
Tool-using agents can fail twice: a required tool can fail, and the agent can then report success without the evidence needed to justify it. Existing benchmarks often entangle this reporting failure with tool selection, recovery, and environment dynamics. We introduce Failure-Transparent Agents (FTA), a controlled benchmark that fixes the failed observation and required evidence state before generation, making post-f
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
Reinforcement learning (RL) in simulation can train dexterous manipulation policies without robot demonstrations, but training a single generalist policy with task-agnostic rewards faces a severe exploration problem: approaching, grasping, and reorienting diverse objects with many degrees of freedom is difficult to discover from scratch. Prior works make exploration tractable with high-quality robot demonstrations, p
Reinforcing Agentic Creativity in Scientific Ideation with Night Science
Large language models (LLMs) excel at structured, verifiable tasks, but their low-entropy bias can produce homogeneous and predictable outputs, limiting their utility for open-ended scientific ideation. Effective discovery, however, spans a broader creative spectrum: from structured day science to loosely structured, serendipitous night science that reaches ideas beyond those typically considered. We introduce AI Nig
Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these needs with separate models and objectives. We model uncertain node embeddings as random graph signals: graph Fourier filters capture structural variation, and a scalar orthogonal-polynomial chaos coordinate captures latent stochastic variation
Distillation Defenses Easily Break After Reinforcement Learning
Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost. Attackers systematically collect a large volume of frontier model reasoning traces and then train (i.e., "distill") their own models on these traces. Existing defenses against distillation attacks are typically evaluated immediately after distillation,
Reasoning with Continuous Latent Diffusion
Continuous diffusion generates complete reasoning solutions through iterative refinement in latent space. We introduce Latent Flow Reasoning Models (LFRMs), an ELF-based training and inference recipe. Our experiments show that accurate decoding alone does not ensure strong reasoning performance. We therefore learn compact representations from multiple layers of a strong autoregressive teacher. Their decomposition als
Report: Progressive Disclosure of Agent Skills
Users of Workday's deployed LLM-based agents often request features which can be addressed by defining named procedures, also known as skills, in the LLM context, effectively augmenting agents' capabilities. However, as an agent's skills library grows in size, so does the agent's operational cost. Progressive disclosure (lazy-loading) of skills as needed may reduce operational costs, but its impact on overall latency
Rethinking Circuit Evaluation: Do Circuits Explain Model Errors?
Mechanistic interpretability (MI) aims to explain a model's behaviour through analyzing its internal computations; circuit-based explanations aim to isolate these computations with compact subnetworks validated by ablating the rest of the model. We show that circuits validated this way may fail to recover the underlying mechanism of the model's behaviour by closely reproducing its successful decisions while failing t
Verifier Errors in RLVR: Reward Hacking, Limits of Feedback, and Selective Control
In reinforcement learning with verifiable rewards (RLVR), imperfect verifiers can reward incorrect responses, creating opportunities for reward hacking. Using gradient flow with a fixed verifier, we characterize the conditions under which reward rises while correctness falls. We then show that the observations available during RLVR are, in general, insufficient to detect or identify accepted errors, or to guarantee t
PhoneCLI: From App Interfaces to Callable Commands for Mobile Agents
Mobile GUI agents operate through a perception--action loop: at each step they screenshot the device, invoke a vision--language model (VLM), and emit an action. It is slow, costly, and brittle, yet most of what it does is navigation---and everyday navigation is static, ordered, and endlessly repeated. We present PhoneCLI, which compiles an app's GUI navigation into callable commands, without any app-internal API, run
Gated Linear Attention (GLA) Transformers advance linear recurrent models through data-dependent gating, but face a core limitation: the fixed-capacity memory matrices across all heads operate at a single temporal resolution, where each token is processed individually, forcing them to simultaneously encode local syntactic patterns and long-range semantic structure, creating a representational bottleneck that gating a
CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search
Population-based optimization methods often use previous search information through successful solutions, parameter adaptation, or operator performance, but they rarely retain the context in which a search behavior succeeded or failed. We introduce Cognitive Memory-Driven Optimization (CMDO), a derivative-free population-based optimizer that represents experience as the relationship between search context, search beh
Large Language Models can perform multi-step reasoning and improve task performance through different forms of intermediate computation, from token-based traces to computation carried out in latent space. However, a question remains open: do these different forms of thinking rely on the same underlying mechanism? To address this, we train and compare five variants of the same GPTNeoX backbone from scratch on an exten
Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts
Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part because it is learned using unreliable reward models such as object detectors and vision-language models. We introduce Verifiable Visual Rewards (VVR), the first framework for programmatically verifiable image rewards, and show that training on it generalizes to natural
GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation
Writing fast GPU code for physical simulation is difficult: implementations must preserve numerical accuracy while handling irregular data access, synchronization, and iterative solvers. We introduce GPUPhysBench, a benchmark of 50 tasks testing whether coding agents can meet these demands. Tasks cover fluids, deformable solids, and granular materials, from individual simulation operators to complete simulators. Agen
DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising
Electrocardiogram (ECG) recordings are corrupted by non-stationary noise sources that degrade diagnostic reliability, particularly in ambulatory and long-duration recordings. Deep learning denoisers exist, but convolutional architectures are limited by their receptive field, transformer-based models scale quadratically with sequence length, and diffusion-based approaches incur prohibitive inference cost. We propose a
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 | 11.4% (114) | 4.9% (49) | 74.55% (82) |
| llms.txt (apex domain) | 9.2% (92) | 3.8% (38) | 60% (66) |
| llms.txt (docs subdomain) | 4% (40) | 1% (10) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 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) | 0.2% (2) | 0.91% (1) |
Notable events recorded on this day and month across all years.