{"date":"2026-08-31","schema_version":1,"captured_at":"2026-09-01T09:43:10.302Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Toxic (2026 film)","views":396590,"rank":4,"extract":"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 in the lead ro","url":"https://en.wikipedia.org/wiki/Toxic_(2026_film)","thumb":"https://upload.wikimedia.org/wikipedia/en/0/0b/Toxic-_A_Fairy_Tale_for_Grown-Ups_poster.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"Dolly Parton","views":372452,"rank":5,"extract":"Dolly Rebecca Parton Dean was an American singer-songwriter, actress and businesswoman. 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authorizations","points":104,"comments":84,"url":"https://www.dailycal.org/news/campus/after-second-ice-threat-uc-berkeley-indefinitely-suspends-international-student-work-authorizations/article_0e9ae0c0-dd4d-4455-a25b-e8be7b47e350.html","discussion":"https://news.ycombinator.com/item?id=49505288"}],"research":[{"title":"SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies","abstract":"Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objectives, learning amortizes that behavior into a reactive policy, yet existing protocols discard task semantics, leaving rewards hand-crafted and behavior drifting from what control verified.We introduce Semantically UNified (SUN) Programs, typed executables where geometric ","authors":["Weiqi Wang","Zhi Li","Yudong Lei","David Martinez","Xiaofeng Gao","Yuxin Jiang"],"url":"https://arxiv.org/abs/2608.31167v1","arxiv_id":"2608.31167v1","published":"2026-08-31T17:59:16Z"},{"title":"Auditing Anonymous AI Models: A Four-Stage Protocol for Black-Box Identity Verification","abstract":"The 2025--2026 AI market has seen a wave of stealth releases: frontier models launched anonymously on developer platforms under codenames. For their users, identity determines data-handling terms, supply-chain risk, and capability expectations. No validated methodology exists for black-box identity verification of anonymous models: practitioner checklists lack accuracy evidence, and self-identification is untrustwort","authors":["Yisen Xi"],"url":"https://arxiv.org/abs/2608.31142v1","arxiv_id":"2608.31142v1","published":"2026-08-31T17:48:24Z"},{"title":"OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques","abstract":"Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding (KGE) models and, more recently, Large Language Model (LLM)-based approaches. Although modern OA frameworks provide unified ecosystems for deploying these heterogeneous aligners, mechanisms for systematically reconciling their complementary and sometimes conflicting p","authors":["Hamed Babaei Giglou","Sören Auer","Peio Popov","Mahsa Sanaei","Jennifer D'Souza"],"url":"https://arxiv.org/abs/2608.31137v1","arxiv_id":"2608.31137v1","published":"2026-08-31T17:44:25Z"},{"title":"When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning","abstract":"The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled evaluation of 13 models spanning dense and Mixture-of-Experts variants from the Qwen3.5 and Qwen3.6 lineages, together with proprietary GPT release variants, using the OntoLearner retrieval-augmented generation pipeline. All models are evaluated with the same embedding mod","authors":["Hamed Babaei Giglou","Sören Auer","Jennifer D'Souza"],"url":"https://arxiv.org/abs/2608.31118v1","arxiv_id":"2608.31118v1","published":"2026-08-31T17:30:05Z"},{"title":"BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing","abstract":"Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of magnitude more interactions than any evaluation can simulate. Automated auditors make testing cheap to scale and flexible enough to cover almost any specified behaviour, yet their lack of optimisation pressure makes them sample-inefficient. To address this shortcoming","authors":["Adrians Skapars","Edoardo Manino"],"url":"https://arxiv.org/abs/2608.31105v1","arxiv_id":"2608.31105v1","published":"2026-08-31T17:10:59Z"},{"title":"LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering","abstract":"Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixture budgets without regressing the rest. The maintained artifact is increasingly dataware: behavior governed by a curated post-training mixture, updated via bounded mixture patches rather than clean-slate retraining. From an industrial code-generation improvement effort,","authors":["Gopi Krishnan Rajbahadur","Amir M. Ebrahimi","Boyuan Chen","Ahmed E. Hassan"],"url":"https://arxiv.org/abs/2608.31102v1","arxiv_id":"2608.31102v1","published":"2026-08-31T17:08:41Z"},{"title":"Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations","abstract":"Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophysical, hybrid, and deep learning models have shown high predictive accuracy when trained on local data but remain largely site-specific. The limited availability of cold hardiness data, coupled with the lack of principled methods for transfe","authors":["William Solow","Paola Pesantez-Cabrera","Markus Keller","Lav Khot","Sandhya Saisubramanian","Alan Fern"],"url":"https://arxiv.org/abs/2608.31097v1","arxiv_id":"2608.31097v1","published":"2026-08-31T17:04:14Z"},{"title":"Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data","abstract":"Valuable data remains embedded in unstructured sources: web pages, reports, contracts, filings, earnings calls, and PDFs. The big bet in enterprise AI is deploying LLM agents that reason over this data to answer complex questions for every knowledge worker. Agents can do this today, but at prohibitive cost. Each question repeatedly opens large documents to recover scattered evidence, consuming up to a million tokens.","authors":["Milad Rezaei Hajidehi","Qitong Wang","Stratos Idreos"],"url":"https://arxiv.org/abs/2608.31082v1","arxiv_id":"2608.31082v1","published":"2026-08-31T16:53:45Z"},{"title":"Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization","abstract":"Outcome-based reinforcement learning provides verified feedback for language-model agents, but assigns trajectory-level advantage uniformly to all decisions, yielding coarse credit over long-horizon interactions. On-policy self-distillation offers finer supervision by re-evaluating sampled behavior with privileged information (PI) available only during training. However, fine-grained supervision is not necessarily fi","authors":["Jingxiao Yang","Wangjie Gan","Yingxuan Zhuang","Wenqi Zhang","Jintao Chen","Xuhong Zhang"],"url":"https://arxiv.org/abs/2608.31077v1","arxiv_id":"2608.31077v1","published":"2026-08-31T16:51:50Z"},{"title":"Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents","abstract":"Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-ended research tasks often do not clearly specify the analyses, methods, and success criteria required to complete the task. As a result, agents may miss important analyses, use inappropriate methods, or draw conclusions th","authors":["Xuehai Wang","Haowei Qin","Tongxin Liu","Junkai Li","Buqiang Xu","Jintian Zhang"],"url":"https://arxiv.org/abs/2608.31076v1","arxiv_id":"2608.31076v1","published":"2026-08-31T16:48:51Z"},{"title":"Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence","abstract":"Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code, where outcomes can be checked automatically. Extending this progress to open-ended and agentic tasks remains difficult because reliable rewards are harder to obtain and direct human supervision cannot keep pace with the scale and complexity","authors":["Zhiqin Yang","Jingwen Fu","Yuhan Liu","Hengyu Liu","Yonggang Zhang","Kainan Cao"],"url":"https://arxiv.org/abs/2608.31075v1","arxiv_id":"2608.31075v1","published":"2026-08-31T16:48:48Z"},{"title":"Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP-Based Semantic Scoring","abstract":"We propose a lightweight two-stage framework for real-time video anomaly detection. The first stage employs YOLO v11n-pose to detect persons and extract seventeen skeletal keypoints in a single forward pass. The second stage encodes each cropped person region through CLIP ViT-B/32 and computes cosine similarity against predefined textual descriptions of anomalous behaviors. This architecture eliminates the need for o","authors":["Vanodhya G. Warnasooriya","Amir Hajian","Watchara Ruangsang","Supavadee Aramvith"],"url":"https://arxiv.org/abs/2608.31074v1","arxiv_id":"2608.31074v1","published":"2026-08-31T16:47:29Z"},{"title":"Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores","abstract":"When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of reasoning with a late-stage output bottleneck. We observe a consistent readout gap across diverse reasoning benchmarks: hidden-state probes successfully decode correct answers even when native sequence scoring completely collapses due to structural biases. To test whe","authors":["Qiyao Yan","Chenpeng Wang","Liangming Pan"],"url":"https://arxiv.org/abs/2608.31068v1","arxiv_id":"2608.31068v1","published":"2026-08-31T16:43:55Z"},{"title":"Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents","abstract":"Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles and exhibit different size, retention, and representation profiles. Recent work has begun to explore memory-management mechanisms that account for such heterogeneity. This work focuses on semantic heterogeneity and studies how it should shape the management and evaluati","authors":["Le Chen","Zishen Wan","Baixi Sun","Xiaolong Ma","Chih-Hsuan Yang","Feng Yan"],"url":"https://arxiv.org/abs/2608.31057v1","arxiv_id":"2608.31057v1","published":"2026-08-31T16:34:51Z"},{"title":"MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents","abstract":"AI agents in partially observable environments need to coordinate active sensing with working memory to maintain an evolving perceptual state. However, existing benchmarks struggle to isolate this perceptual-state construction and interpretation capability because they introduce physical and control complexities. We address this with MNIST-PRO, a benchmark that isolates agentic perception by converting MNIST digit re","authors":["Vernon Toh","Navonil Majumder","Zhengyuan Liu","Nancy F. Chen","Soujanya Poria"],"url":"https://arxiv.org/abs/2608.31022v1","arxiv_id":"2608.31022v1","published":"2026-08-31T16:05:39Z"},{"title":"One note in three: a verified census of three deployed AI scribes, and the instrument that counted it","abstract":"Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 142 consultations: 565 notes from recorded UK primary-care and US ambulatory encounters plus authored scenarios. Twelve discovery passes proposed 13,678 candidate errors; the 5,898 clearing an importance filter went to an adversarial panel of two models from different fam","authors":["Sebastian Fox","Luke Markham","Ryan Lail","Michael Karotsieris"],"url":"https://arxiv.org/abs/2608.31017v1","arxiv_id":"2608.31017v1","published":"2026-08-31T16:00:27Z"},{"title":"LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It","abstract":"Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the note fails to record. The standard check is an LLM judge: a second model reads the note against the transcript and flags problems. We ask whether judges detect omissions. Public corpora cannot supply the answer key: their clinician reference notes and transcripts are mate","authors":["Sebastian Fox","Luke Markham","Ryan Lail","Michael Karotsieris"],"url":"https://arxiv.org/abs/2608.31016v1","arxiv_id":"2608.31016v1","published":"2026-08-31T15:59:54Z"},{"title":"Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning","abstract":"Supervised fine-tuning (SFT) trains a base language model to imitate target responses, and these targets may require knowledge the base model has not robustly internalized. We study this as a source of hallucinations and frame a group of mitigation methods as \\emph{knowledge-aligned SFT}: constraining SFT training targets to the base model's parametric knowledge. Under a unified setup, we compare existing generation-","authors":["Arthur Becker","Jakob Kemmler","David Thulke","Christine Schäfer","Christian Dugast","Hermann Ney"],"url":"https://arxiv.org/abs/2608.30987v1","arxiv_id":"2608.30987v1","published":"2026-08-31T15:43:51Z"},{"title":"Evaluating and Improving LLM Self-Modeling","abstract":"We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a prompt edit would change the model's final answer. To measure this capability, we introduce a benchmark that tests diverse types of self-modeling questions. Current models show non-trivial but limited self-modeling skill, and make systematic mistakes on simple counterfact","authors":["Siqi Zeng","Andre N. Assis","Rowan Wang"],"url":"https://arxiv.org/abs/2608.30980v1","arxiv_id":"2608.30980v1","published":"2026-08-31T15:37:51Z"},{"title":"MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI","abstract":"Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep learning approaches process individual 2D slices, discarding this context. We present MR-JEPA, a self-supervised video foundation model for CMR that extends LeJEPA to 3D spatiotemporal inputs through tubelet tokenization, spatiotemporal masking augmentation, and initializat","authors":["Athira J. Jacob","Puneet Sharma","Dorin Comaniciu","Daniel Rueckert"],"url":"https://arxiv.org/abs/2608.30975v1","arxiv_id":"2608.30975v1","published":"2026-08-31T15:36:24Z"},{"title":"CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations","abstract":"Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in latent space. However, it typically relies on teacher--student architecture with an EMA to stabilise training, and can tend to yield uninformative representations. Contrastive learning is stable to train and produces strong global representations, but remains limited on lo","authors":["Gabriel Meseguer-Brocal","Yuexuan Kong","Romain Hennequin"],"url":"https://arxiv.org/abs/2608.30974v1","arxiv_id":"2608.30974v1","published":"2026-08-31T15:36:13Z"},{"title":"CogEvol: Towards Efficient and Reliable Learning Environment Generation","abstract":"We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass. Across 220k production requests, CogEvol completes a slide in a median of 17 seconds and an interactive page in 59, replacing minutes-long multi-turn agent scaffolding. Reliability i","authors":["Shangqing Tu","Daniel Zhang-Li","Yucheng Wang","Shiyu Gan","Yanpeng Wang","Huiqiang Rong"],"url":"https://arxiv.org/abs/2608.30968v1","arxiv_id":"2608.30968v1","published":"2026-08-31T15:33:38Z"},{"title":"A Universal Context-Reuse Layer for Cross-Model KV Sharing","abstract":"Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own prefill computation even when another model has already processed the same input. Existing KV-cache reuse mechanisms substantially reduce redundant computation within a single model, but generally assume that the producer and consumer of a cache are identical. We study \\emp","authors":["Yi Li","Dongming Jiang","Yi Zhao","Bingzhe Li"],"url":"https://arxiv.org/abs/2608.30963v1","arxiv_id":"2608.30963v1","published":"2026-08-31T15:28:17Z"},{"title":"LOCI: A Locator-Critic with Refinement Loop","abstract":"Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locate critical details in the image. Due to this shortcoming, VLMs generate often plausible but incorrect reasoning based on flawed perceptual grounding. To address this, we propose Locator-Critic (LOCI), a training-free framework that decouple","authors":["Walid Bousselham","Mathilde Caron","Arsha Nagrani","Cordelia Schmid"],"url":"https://arxiv.org/abs/2608.30959v1","arxiv_id":"2608.30959v1","published":"2026-08-31T15:26:00Z"},{"title":"Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse","abstract":"Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. This technique is used for a range of tasks such as debugging models, explaining predictions, justifying decisions, and providing algorithmic recourse. In this paper, we explore the normative legitimacy of employing counterfactuals in real-lif","authors":["Mattia Cerrato","Otto Sahlgren","Xenia Heilmann"],"url":"https://arxiv.org/abs/2608.30956v1","arxiv_id":"2608.30956v1","published":"2026-08-31T15:24:19Z"}],"history":{"events":[{"year":2019,"text":"A sightseeing helicopter crashed in the mountains of Skoddevarre in Alta, Norway, killing all six people on board.","url":"https://en.wikipedia.org/wiki/2019_Alta_helicopter_crash"},{"year":2019,"text":"A sightseeing helicopter crashed in the mountains of Skoddevarre in Alta, Norway, killing all six people on board.","url":"https://en.wikipedia.org/wiki/2019_Alta_helicopter_crash"},{"year":2010,"text":"The last episode of The Bill, the longest-running police drama in British television history, was broadcast.","url":"https://en.wikipedia.org/wiki/The_Bill"},{"year":2002,"text":"Typhoon Rusa made landfall in Goheung as the most powerful typhoon to hit South Korea in 43 years, killing at least 236 people.","url":"https://en.wikipedia.org/wiki/Typhoon_Rusa"},{"year":1997,"text":"Diana, Princess of Wales (pictured), her partner Dodi Fayed, and their driver were killed in a car crash in Paris.","url":"https://en.wikipedia.org/wiki/Diana%2C_Princess_of_Wales"},{"year":1969,"text":"On the final day of the Isle of Wight Festival 1969, attended by approximately 150,000 people over three days, Bob Dylan appeared in his first gig in three years.","url":"https://en.wikipedia.org/wiki/Isle_of_Wight_Festival_1969"},{"year":1959,"text":"A parcel bomb sent by Ngô Đình Nhu (pictured), younger brother and chief adviser of South Vietnamese president Ngô Đình Diệm, failed to kill Norodom Sihanouk, Prime Minister of Cambodia.","url":"https://en.wikipedia.org/wiki/Letter_bomb"},{"year":1942,"text":"The Matagorda hurricane, the most intense and costliest tropical cyclone of the 1942 Atlantic hurricane season, dissipated after causing $26.5 million in damages and eight deaths.","url":"https://en.wikipedia.org/wiki/1942_Matagorda_hurricane"},{"year":1941,"text":"World War II: A detachment of Chetniks captured the town of Loznica in German-occupied Serbia.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1940,"text":"Second World War: Two Royal navy destroyers were lost to mines and two other vessels damaged off Texel, Netherlands, killing around 300 men.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1939,"text":"Nazi forces, posing as Poles, staged an attack against the German radio station Sender Gleiwitz in Gleiwitz, Upper Silesia, Germany, creating an excuse to invade Poland the next day.","url":"https://en.wikipedia.org/wiki/Gleiwitz_incident"},{"year":1897,"text":"Thomas Edison was granted a patent for the Kinetoscope, a precursor to the modern movie projector.","url":"https://en.wikipedia.org/wiki/Thomas_Edison"},{"year":1897,"text":"Thomas Edison was granted a patent for the Kinetoscope (pictured), a precursor to the modern movie projector.","url":"https://en.wikipedia.org/wiki/Thomas_Edison"},{"year":1886,"text":"The 7.0 Mw Charleston earthquake struck southeastern South Carolina with a maximum Mercalli intensity of X (Extreme), killing 60 people and causing damage worth an estimated $5–6 million.","url":"https://en.wikipedia.org/wiki/1886_Charleston_earthquake"},{"year":1876,"text":"Sultan Murad V of the Ottoman Empire was deposed after a reign of 93 days on grounds of mental illness.* 1888 – The body of Mary Ann Nichols, the alleged first victim of an unidentified serial killer known as Jack the Ripper (pictured), was found in Buck's Row, London.","url":"https://en.wikipedia.org/wiki/Murad_V"},{"year":1814,"text":"War of 1812: American militia forces repelled a British force at the Battle of Caulk's Field, killing 41 people including Captain Peter Parker of the Royal Navy.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1814,"text":"War of 1812: American militia forces repelled a British force at the Battle of Caulk's Field, killing 41 people, including Captain Peter Parker of the Royal Navy.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1218,"text":"Al-Kamil became the fourth sultan of the Ayyubid dynasty of Egypt.","url":"https://en.wikipedia.org/wiki/Al-Kamil"}],"births":[{"year":2004,"text":"Jang Won-young, South Korean singer and model","url":"https://en.wikipedia.org/wiki/Jang_Won-young"},{"year":2001,"text":"Amanda Anisimova, American tennis player","url":"https://en.wikipedia.org/wiki/Amanda_Anisimova"},{"year":2000,"text":"Sauce Gardner, American football player","url":"https://en.wikipedia.org/wiki/Sauce_Gardner"},{"year":1998,"text":"BossMan Dlow, American rapper","url":"https://en.wikipedia.org/wiki/BossMan_Dlow"},{"year":1997,"text":"Jaylen Barron, American actress","url":"https://en.wikipedia.org/wiki/Jaylen_Barron"},{"year":1996,"text":"Jalen Brunson, American basketball player","url":"https://en.wikipedia.org/wiki/Jalen_Brunson"},{"year":1995,"text":"Brittany Mahomes, American soccer player","url":"https://en.wikipedia.org/wiki/Brittany_Mahomes"},{"year":1994,"text":"Alex Harris, Scottish footballer","url":"https://en.wikipedia.org/wiki/Alex_Harris_(Scottish_footballer)"}],"deaths":[{"year":2024,"text":"Sol Bamba, Ivorian-French footballer (born 1985)","url":"https://en.wikipedia.org/wiki/Sol_Bamba"},{"year":2023,"text":"Silvina Luna, Argentine model and actress (born 1980)","url":"https://en.wikipedia.org/wiki/Silvina_Luna"},{"year":2021,"text":"Mahal, Filipino comedian and actress (born 1974)","url":"https://en.wikipedia.org/wiki/Mahal_(actress)"},{"year":2021,"text":"Francesco Morini, Italian footballer (born 1944)","url":"https://en.wikipedia.org/wiki/Francesco_Morini"},{"year":2021,"text":"Michael Constantine, Greek-American actor (born 1927)","url":"https://en.wikipedia.org/wiki/Michael_Constantine"},{"year":2021,"text":"Geronimo, British alpaca (born 2013)","url":"https://en.wikipedia.org/wiki/Geronimo_(alpaca)"},{"year":2020,"text":"Pranab Mukherjee, Former President of India (born 1935)","url":"https://en.wikipedia.org/wiki/Pranab_Mukherjee"},{"year":2020,"text":"Tom Seaver, American baseball player (born 1944)","url":"https://en.wikipedia.org/wiki/Tom_Seaver"}]},"census":{"panel_version":2,"probed":14770,"error_rate":29.7,"degraded":false,"rates":{"head":11.2,"tail":4.4,"ai_native":75.45}},"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 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Every item links to its source."}