{"date":"2026-09-28","schema_version":1,"captured_at":"2026-09-29T10:58:51.717Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Lizzie Borden","views":440478,"rank":4,"extract":"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.","url":"https://en.wikipedia.org/wiki/Lizzie_Borden","thumb":"https://thumb.wikimedia.org/wikipedia/commons/thumb/7/71/Lizzie_Borden_1890.jpg/330px-Lizzie_Borden_1890.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Ted Kaczynski","views":438924,"rank":5,"extract":"Theodore John Kaczynski, also known as the Unabomber, was an American mathematician and domestic terrorist. 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What the data suggests","points":227,"comments":285,"url":"https://www.petervijeh.com/projects/reddit-astroturf","discussion":"https://news.ycombinator.com/item?id=49877678"},{"title":"Prompting Claude Opus 5.5","points":203,"comments":221,"url":"https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5-5","discussion":"https://news.ycombinator.com/item?id=49874728"},{"title":"Musk, the Movie","points":190,"comments":155,"url":"https://bleeckerstreetmedia.com/musk","discussion":"https://news.ycombinator.com/item?id=49872766"},{"title":"Flock Wants the Most Detailed Map of Its Surveillance Cameras Taken Offline","points":190,"comments":88,"url":"https://theintercept.com/2026/09/24/how-many-flock-devices-in-united-states-300000/","discussion":"https://news.ycombinator.com/item?id=49884363"},{"title":"Show HN: HN.watch – Videos of all Hacker News posts","points":183,"comments":95,"url":"https://hn.watch/","discussion":"https://news.ycombinator.com/item?id=49879401"},{"title":"Nvidia wants to put a watchdog chip next to every AI agent","points":178,"comments":225,"url":"https://www.cnbc.com/2026/09/28/nvidia-releases.html","discussion":"https://news.ycombinator.com/item?id=49879883"}],"research":[{"title":"FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets","abstract":"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","authors":["Srinjay Sarkar","Prakhar Kaushik","Soumava Paul","Alan Yuille"],"url":"https://arxiv.org/abs/2609.35770v1","arxiv_id":"2609.35770v1","published":"2026-09-28T17:59:58Z"},{"title":"Telescopic Language Models","abstract":"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","authors":["Zhilin Guo","Boqiao Zhang","Hakan Aktas","Kyle Fogarty","Nursena Koprucu Aslan","Wenzhao Li"],"url":"https://arxiv.org/abs/2609.35769v1","arxiv_id":"2609.35769v1","published":"2026-09-28T17:59:53Z"},{"title":"Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning","abstract":"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 ","authors":["Yijia Fan","Ziqi Huang","Zhongang Cai","Yan Li","Zimo Wen","Wanqi Yin"],"url":"https://arxiv.org/abs/2609.35767v1","arxiv_id":"2609.35767v1","published":"2026-09-28T17:59:36Z"},{"title":"TokenCast: Forecasting Token Consumption During LLM Agent Execution","abstract":"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","authors":["Chaoqian Ouyang","Ling Yue","Libin Zheng","Huanghui Guo","Shengxiang Xu","YiShu Wang"],"url":"https://arxiv.org/abs/2609.35760v1","arxiv_id":"2609.35760v1","published":"2026-09-28T17:59:09Z"},{"title":"How to Loop MoE: Flatten the Experts, Untie the Attention","abstract":"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?","authors":["Shouren Wang","Chuang Ma","Mohsen Hariri","Debargha Ganguly","Wang Yang","Xiaoqing Tong"],"url":"https://arxiv.org/abs/2609.35751v1","arxiv_id":"2609.35751v1","published":"2026-09-28T17:58:03Z"},{"title":"KV-streams for Efficient Compaction in Agentic Reinforcement Learning","abstract":"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","authors":["Emiliano Penaloza","Dane Malenfant","Dheeraj Vattikonda","Roger Creus Castanyer","Siddarth Venkatraman","Abhay Puri"],"url":"https://arxiv.org/abs/2609.35750v1","arxiv_id":"2609.35750v1","published":"2026-09-28T17:57:42Z"},{"title":"Copy the Same, Distill the Difference: Initializing Linear Vision Transformers","abstract":"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","authors":["Huaiyuan Qin","Muli Yang","Gabriel James Goenawan","Shiqi Huang","Min Kass Chong","Wahyu Wiratama"],"url":"https://arxiv.org/abs/2609.35745v1","arxiv_id":"2609.35745v1","published":"2026-09-28T17:55:21Z"},{"title":"FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents","abstract":"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","authors":["Hoyoung Lee","Suyeol Yun","Jack Haverty","Yunju Cho","Meesong Kim","Daekyung Park"],"url":"https://arxiv.org/abs/2609.35744v1","arxiv_id":"2609.35744v1","published":"2026-09-28T17:55:06Z"},{"title":"Shockingly Simple Self-retrospection Improves Agentic Models Without RL","abstract":"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","authors":["Jonathan Light","Christopher Zhang Cui","Jeonghye Kim","Roger Creus Castanyer","Emiliano Penaloza","Zhengyan Shi"],"url":"https://arxiv.org/abs/2609.35741v1","arxiv_id":"2609.35741v1","published":"2026-09-28T17:54:24Z"},{"title":"Failure-Transparent Agents: Benchmarking Post-Failure Reporting in Tool-Using Language Models","abstract":"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","authors":["Junru Zhu","Shiming Xie","Aime Lu Fan Chen","Xiaoqing Ding","Chunxin Tang","Ruoyu Qi"],"url":"https://arxiv.org/abs/2609.35732v1","arxiv_id":"2609.35732v1","published":"2026-09-28T17:51:41Z"},{"title":"X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets","abstract":"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","authors":["Prithwish Dan","Chenyang Ma","Wei Zhan"],"url":"https://arxiv.org/abs/2609.35715v1","arxiv_id":"2609.35715v1","published":"2026-09-28T17:46:42Z"},{"title":"Reinforcing Agentic Creativity in Scientific Ideation with Night Science","abstract":"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","authors":["Priyanka Kargupta","Silviu Cucerzan","Shweti Mahajan","Allen Herring","Jiawei Han","Ryen W. White"],"url":"https://arxiv.org/abs/2609.35706v1","arxiv_id":"2609.35706v1","published":"2026-09-28T17:43:52Z"},{"title":"A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion","abstract":"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","authors":["Fred Xu","Thomas Markovich","Florence Regol","Yizhou Sun"],"url":"https://arxiv.org/abs/2609.35703v1","arxiv_id":"2609.35703v1","published":"2026-09-28T17:42:52Z"},{"title":"Distillation Defenses Easily Break After Reinforcement Learning","abstract":"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,","authors":["Shidan Javaheri","Alexander Panfilov","Oliver Britton","Yarin Gal","Yonatan Gideoni"],"url":"https://arxiv.org/abs/2609.35699v1","arxiv_id":"2609.35699v1","published":"2026-09-28T17:40:32Z"},{"title":"Reasoning with Continuous Latent Diffusion","abstract":"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","authors":["Xiang Cheng"],"url":"https://arxiv.org/abs/2609.35694v1","arxiv_id":"2609.35694v1","published":"2026-09-28T17:38:43Z"},{"title":"Report: Progressive Disclosure of Agent Skills","abstract":"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","authors":["Guilin Zhang","Kai Zhao","Priyanka Mudgal","Waleed Ammar","Xiquan Cui","Xu Chu"],"url":"https://arxiv.org/abs/2609.35692v1","arxiv_id":"2609.35692v1","published":"2026-09-28T17:38:37Z"},{"title":"Rethinking Circuit Evaluation: Do Circuits Explain Model Errors?","abstract":"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","authors":["Li Zhang","Chuqin Geng","Mark Zhang","Chen Yang","Luke Zhang","Haolin Ye"],"url":"https://arxiv.org/abs/2609.35686v1","arxiv_id":"2609.35686v1","published":"2026-09-28T17:34:45Z"},{"title":"Verifier Errors in RLVR: Reward Hacking, Limits of Feedback, and Selective Control","abstract":"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","authors":["Christian Moya","Elliott Thornley","Guang Lin"],"url":"https://arxiv.org/abs/2609.35677v1","arxiv_id":"2609.35677v1","published":"2026-09-28T17:30:24Z"},{"title":"PhoneCLI: From App Interfaces to Callable Commands for Mobile Agents","abstract":"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","authors":["Yangqin Jiang","Lingrui Xu","Chao Huang"],"url":"https://arxiv.org/abs/2609.35671v1","arxiv_id":"2609.35671v1","published":"2026-09-28T17:26:56Z"},{"title":"MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution","abstract":"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","authors":["Prasoon Dev","Anirudh Sankar","Vasudeva Varma"],"url":"https://arxiv.org/abs/2609.35664v1","arxiv_id":"2609.35664v1","published":"2026-09-28T17:23:45Z"},{"title":"CMDO: A Cognitive Memory-Driven Optimization Algorithm for Adaptive Population-Based Search","abstract":"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","authors":["Mohammed Yusuf Mujawar","Shahram Rahimi","Noorbakhsh Amiri Golilarz"],"url":"https://arxiv.org/abs/2609.35657v1","arxiv_id":"2609.35657v1","published":"2026-09-28T17:21:43Z"},{"title":"Not All Thinking is Created Equal: Latent Reasoning Discovers a Recurrent Search Algorithm for Depth Generalization","abstract":"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","authors":["Huzi Cheng","Zhewei Zhang"],"url":"https://arxiv.org/abs/2609.35643v1","arxiv_id":"2609.35643v1","published":"2026-09-28T17:12:30Z"},{"title":"Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts","abstract":"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","authors":["Shuyue Stella Li","Xiaochuang Han","Yulia Tsvetkov","Luke Zettlemoyer"],"url":"https://arxiv.org/abs/2609.35641v1","arxiv_id":"2609.35641v1","published":"2026-09-28T17:07:49Z"},{"title":"GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation","abstract":"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","authors":["Yuchen Sun","Jinjin He","Sinan Wang","Bo Zhu"],"url":"https://arxiv.org/abs/2609.35639v1","arxiv_id":"2609.35639v1","published":"2026-09-28T17:06:44Z"},{"title":"DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising","abstract":"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","authors":["Basile Morel","Samuel Ruiperez-Campillo","Andreas P. Streich","Julia E. Vogt","Thomas Hofmann"],"url":"https://arxiv.org/abs/2609.35634v1","arxiv_id":"2609.35634v1","published":"2026-09-28T17:03:04Z"}],"history":{"events":[{"year":2023,"text":"The Sycamore Gap tree (pictured) in Northumberland, England, was illegally felled.","url":"https://en.wikipedia.org/wiki/Sycamore_Gap_tree"},{"year":2012,"text":"War in Somalia: Somali National Army forces and their AMISOM and Raskamboni allies launched an offensive against Al-Shabaab in the latter's last major stronghold of Kismayo.","url":"https://en.wikipedia.org/wiki/Somali_Civil_War_(2009%E2%80%93present)"},{"year":2009,"text":"A protest held by 50,000 people in Conakry, Guinea, was forcefully disrupted by the military junta, resulting in at least 157 deaths and over 1,200 injuries.","url":"https://en.wikipedia.org/wiki/2009_Guinean_protests"},{"year":2006,"text":"Typhoon Xangsane passed Manila on its way to causing more than 300 deaths, mostly in the Phillippines and Vietnam.","url":"https://en.wikipedia.org/wiki/Typhoon_Xangsane"},{"year":1978,"text":"Pope John Paul I died only 33 days after his papal election due to an apparent myocardial infarction, resulting in the first year of three popes since 1605.","url":"https://en.wikipedia.org/wiki/Pope_John_Paul_I"},{"year":1975,"text":"An attempted robbery of Spaghetti House, a restaurant in Knightsbridge, London, turned into a six-day hostage situation.","url":"https://en.wikipedia.org/wiki/Knightsbridge"},{"year":1972,"text":"Against the backdrop of the Cold War, the Canadian ice hockey team defeated the Soviet team in the Summit Series.","url":"https://en.wikipedia.org/wiki/Cold_War"},{"year":1963,"text":"Whaam!, now considered one of Roy Lichtenstein's most important works, debuted at an exhibition held at the Leo Castelli Gallery, New York City.","url":"https://en.wikipedia.org/wiki/Whaam!"},{"year":1958,"text":"Fernando Rios, a Mexican tour guide in New Orleans, was killed in an instance of gay bashing.","url":"https://en.wikipedia.org/wiki/New_Orleans"},{"year":1928,"text":"Scottish biologist and pharmacologist Alexander Fleming (pictured) discovered penicillin when he noticed a bacteria-killing mould growing in his laboratory.","url":"https://en.wikipedia.org/wiki/Alexander_Fleming"},{"year":1924,"text":"A team of U.S. Army Air Service aviators landed in Seattle, Washington, to complete the first aerial circumnavigation of the world.","url":"https://en.wikipedia.org/wiki/United_States_Army_Air_Service"},{"year":1901,"text":"Philippine–American War: Filipino guerrillas killed more than forty American soldiers in a surprise attack on the town of Balangiga on the island of Samar.","url":"https://en.wikipedia.org/wiki/Philippine%E2%80%93American_War"},{"year":1821,"text":"The Declaration of Independence of the Mexican Empire from Spain was drafted in the National Palace in Mexico City.","url":"https://en.wikipedia.org/wiki/Declaration_of_Independence_(Mexico)"},{"year":1106,"text":"In the Battle of Tinchebray in Normandy, the invading King Henry I of England captured his brother Robert Curthose.","url":"https://en.wikipedia.org/wiki/Battle_of_Tinchebray"},{"year":1066,"text":"William the Conqueror and his fleet of around 600 ships landed at Pevensey, Sussex, beginning the Norman conquest of England.","url":"https://en.wikipedia.org/wiki/William_the_Conqueror"},{"year":351,"text":"The Eastern Roman armies under Constantius II defeated those of the usurper Magnentius at the Battle of Mursa Major.","url":"https://en.wikipedia.org/wiki/Constantius_II"},{"year":235,"text":"Pope Pontian resigned after being exiled to Sardinia, becoming the first pope to relinquish the position.","url":"https://en.wikipedia.org/wiki/Pope_Pontian"},{"year":-48,"text":"Pompey was killed by Lucius Septimius at Pelusium in Egypt.","url":"https://en.wikipedia.org/wiki/Pompey"}],"births":[{"year":2004,"text":"Isack Hadjar, French-Algerian racing driver","url":"https://en.wikipedia.org/wiki/Isack_Hadjar"},{"year":2000,"text":"Frankie Jonas, American actor, singer, and songwriter","url":"https://en.wikipedia.org/wiki/Frankie_Jonas"},{"year":1999,"text":"Kayla Day, American tennis player","url":"https://en.wikipedia.org/wiki/Kayla_Day"},{"year":1998,"text":"Panna Udvardy, Hungarian tennis player","url":"https://en.wikipedia.org/wiki/Panna_Udvardy"},{"year":1996,"text":"Aiden Moffat, British race car driver","url":"https://en.wikipedia.org/wiki/Aiden_Moffat"},{"year":1995,"text":"Juancho Hernangómez, Spanish basketball player","url":"https://en.wikipedia.org/wiki/Juancho_Hernang%C3%B3mez"},{"year":1995,"text":"Caleb Martin, American basketball player","url":"https://en.wikipedia.org/wiki/Caleb_Martin_(basketball)"},{"year":1995,"text":"Cody Martin, American basketball player","url":"https://en.wikipedia.org/wiki/Cody_Martin_(basketball)"}],"deaths":[{"year":2026,"text":"Siti Hasmah, Malaysian physician and civil servant (born 1926)","url":"https://en.wikipedia.org/wiki/Siti_Hasmah_Mohamad_Ali"},{"year":2024,"text":"Winfield Dunn, American politician, 43rd Governor of Tennessee (born 1927)","url":"https://en.wikipedia.org/wiki/Winfield_Dunn"},{"year":2024,"text":"Drake Hogestyn, American actor (born 1953)","url":"https://en.wikipedia.org/wiki/Drake_Hogestyn"},{"year":2024,"text":"Kris Kristofferson, American singer, songwriter, and actor (born 1936)","url":"https://en.wikipedia.org/wiki/Kris_Kristofferson"},{"year":2022,"text":"Coolio, American rapper (born 1963)","url":"https://en.wikipedia.org/wiki/Coolio"},{"year":2019,"text":"José José, Mexican musician and singer (born 1948)","url":"https://en.wikipedia.org/wiki/Jos%C3%A9_Jos%C3%A9"},{"year":2018,"text":"Predrag Ejdus, Serbian actor (born 1947)","url":"https://en.wikipedia.org/wiki/Predrag_Ejdus"},{"year":2017,"text":"Daniel Pe'er, Israeli television host and newsreader (born 1943)","url":"https://en.wikipedia.org/wiki/Daniel_Pe'er"}]},"census":{"panel_version":2,"probed":14770,"error_rate":27.56,"degraded":false,"rates":{"head":11.4,"tail":4.9,"ai_native":74.55}},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","license":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","license":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"license":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}