{"date":"2026-10-15","schema_version":1,"captured_at":"2026-10-16T09:15:25.295Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"Katie Zacharia","views":906890,"rank":2,"extract":"Kate Sharon Zacharia is an American political commentator who has served as the White House press secretary since October 2026.","url":"https://en.wikipedia.org/wiki/Katie_Zacharia","thumb":"https://thumb.wikimedia.org/wikipedia/commons/thumb/9/9a/Press_Secretary_Katie_Zacharia_holds_first_press_briefing_in_the_James_S_Brady_Press_Briefing_Room%2C_October_13%2C_2026_%28P20261013AH-0626%29_%28cropped%29.jpg/330px-Press_Secretary_Katie_Zacharia_holds_first_press_briefing_in_the_James_S_Brady_Press_Briefing_Room%2C_October_13%2C_2026_%28P20261013AH-0626%29_%28cropped%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"RJ Mitte","views":409408,"rank":5,"extract":"Roy Frank \"RJ\" Mitte III was an American actor and disability rights activist. 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While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM s","authors":["Zhixu Li","Keqian Tang","Litian Gong","Jingyu Yao","Wenqian Zhang","Tianze Xu"],"url":"https://arxiv.org/abs/2610.22372v1","arxiv_id":"2610.22372v1","published":"2026-10-15T17:59:59Z"},{"title":"Discovering state-of-the-art quantum circuits with large language models","abstract":"Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of computational primitives, optimizing them in the quantum setting presents unique challenges, includ","authors":["Andi Gu","Alek H. Kemeny","Susanne F. Yelin","Mikhail D. Lukin"],"url":"https://arxiv.org/abs/2610.22367v1","arxiv_id":"2610.22367v1","published":"2026-10-15T17:59:20Z"},{"title":"T2-z: Test-Time Optimization of Environment Latents for Robotic World Models","abstract":"Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules. We introduce T2-Z for test-time optimization of environment latents, which augments a robotic world model with a learnable environment lat","authors":["Chenyuan Zhou","Yining Hong","Huang Huang","Yunfan Jiang","Wenlong Huang","David Held"],"url":"https://arxiv.org/abs/2610.22364v1","arxiv_id":"2610.22364v1","published":"2026-10-15T17:59:11Z"},{"title":"CoDA: Co-Evolution of Data Engines and Action Policies","abstract":"Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them. We introduce CoDA, a framework in which a simulation data engine and an action policy co-evolve through a shared heterogeneous memory of environment construct","authors":["Tianjiao Yu","Zhilin Ou","Yifan Shen","Xinzhuo Li","Yuanzhe Liu","Onkar Kishor Susladkar"],"url":"https://arxiv.org/abs/2610.22362v1","arxiv_id":"2610.22362v1","published":"2026-10-15T17:59:02Z"},{"title":"Rethinking Routing Exploration in Modern Mixture-of-Experts","abstract":"Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and the latter introduces experts that are active for every token. We identify an exploration dilemma b","authors":["Bingcong Li","Hao Ma","Michael Muehlebach","Niao He","Yi Ma"],"url":"https://arxiv.org/abs/2610.22353v1","arxiv_id":"2610.22353v1","published":"2026-10-15T17:58:02Z"},{"title":"FDE-Gym: The Last Mile of AI Delivery","abstract":"Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in live systems, and uncover unstated constraints. We introduce FDE-Gym, a benchmark of 95 end-to-end delivery tasks from 20 customer scenarios across 18 sectors, eac","authors":["Dongxu Yang","Hongda Yan","Renxin Wang","Baoli Zhang","Haojia Zuo","Ying Zhao"],"url":"https://arxiv.org/abs/2610.22350v1","arxiv_id":"2610.22350v1","published":"2026-10-15T17:57:23Z"},{"title":"MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering","abstract":"Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learning engineering (MLE) systems provide a practical path toward this pursuit through complex orchestration that manages long-horizon experimentation, while recent M","authors":["Shangheng Du","Jinxin Shi","Shuaiyu Zhang","Yiming Wu","Junpeng Zhao","Shiyang Feng"],"url":"https://arxiv.org/abs/2610.22344v1","arxiv_id":"2610.22344v1","published":"2026-10-15T17:56:32Z"},{"title":"GeoState: Learning View-Decoupled Geometry States for 3D World Modeling","abstract":"Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variation, making the resulting state evolution harder to predict. We propose GeoState, a view-decoupled geometry representation for predictive 3D world modeling that ","authors":["Hongchang Liu","Jian Liu","Yujuan Ding","Liang Peng","Xun Jiang","Fei Ma"],"url":"https://arxiv.org/abs/2610.22343v1","arxiv_id":"2610.22343v1","published":"2026-10-15T17:56:18Z"},{"title":"3DCodeVerse: Exploring 3D Worlds as Code","abstract":"We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. First, we construct 3DCodeVerse-2M, a large-scale curated dataset of standalone 3D executable code. We bui","authors":["Yipeng Gao","Ziyao Zeng","Jinfa Huang","Yijiang Li","Haolin Xiong","Wang Qin"],"url":"https://arxiv.org/abs/2610.22341v1","arxiv_id":"2610.22341v1","published":"2026-10-15T17:56:07Z"},{"title":"Dexterity from Supervised Play","abstract":"How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model. During inference, the same play policy then guides model-predictive control","authors":["Botao He","Yiming Dou","Yiannis Aloimonos","Jitendra Malik","Akash Sharma","Ruoshi Liu"],"url":"https://arxiv.org/abs/2610.22340v1","arxiv_id":"2610.22340v1","published":"2026-10-15T17:55:50Z"},{"title":"Evaluation Awareness Beyond English: Do LLMs Act Differently When Tested in Indic Languages?","abstract":"Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation awareness almost entirely in English, although models serve users in many languages. Indic languages are spoken by over a billion people, yet evaluation awareness remains almost entir","authors":["Trizal Garg","Sibayan Mitra","Arya Karanjkar","Advait Singh","Jagat Sesh Challa","Yash Sinha"],"url":"https://arxiv.org/abs/2610.22333v1","arxiv_id":"2610.22333v1","published":"2026-10-15T17:53:33Z"},{"title":"KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents","abstract":"Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision methods aim to assign credit at finer granularity, but they typically anchor credit to either surface","authors":["Siqi Chen","Zhengxi Lu","Yuchen Yan","Xingyu Wu","Qipeng Chen","Xin Zhang"],"url":"https://arxiv.org/abs/2610.22331v1","arxiv_id":"2610.22331v1","published":"2026-10-15T17:52:33Z"},{"title":"Designing Transformers for Continuous Diffusion Language Models","abstract":"Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architectures from either causal large language models (LLMs) or Diffusion Transformers (DiTs) for image generation, which were not optimized for CDLMs. In this paper, we conduct a systematic study on Transformer architectures for CDLMs and introduce Langu","authors":["Weijian Luo","Yujian Chen","Hao Jiang","Guang Cheng","Tianyang Hu"],"url":"https://arxiv.org/abs/2610.22330v1","arxiv_id":"2610.22330v1","published":"2026-10-15T17:52:31Z"},{"title":"ACEdit: Stable Multi-Turn Image Editing via Asymmetric Conditioning","abstract":"Image editing models produce impressive results for individual edits, but their quality often deteriorates over multiple editing turns. Each editing turn introduces small distribution shifts that accumulate over successive edits, creating a gap between the input distributions encountered during the editor's original training and multi-turn inference. We introduce a training scheme that makes multi-turn image editing ","authors":["Sara Dorfman","Nir Goren","Gal Barak","Daniel Cohen-Or","Or Patashnik"],"url":"https://arxiv.org/abs/2610.22328v1","arxiv_id":"2610.22328v1","published":"2026-10-15T17:52:24Z"},{"title":"Twice Upon a Time: Complementary Representations for Time-Series Reasoning","abstract":"Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-series representations preserve, and what variation should they factor out? Existing TSLMs typically expose the language model to a single representation, imposing a fixed inductive bias across downstream tasks. We instead introduce complement","authors":["Hedi Zisling","Liran Nochumsohn","Ilan Naiman","Mihir Parmar","Omri Azencot"],"url":"https://arxiv.org/abs/2610.22327v1","arxiv_id":"2610.22327v1","published":"2026-10-15T17:52:18Z"},{"title":"Learning Self-Orchestration in Decentralized Agent Swarms","abstract":"The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralize","authors":["Guowei Xu","Zhenting Qi","Amani Kiruga","Himabindu Lakkaraju","Yilun Du"],"url":"https://arxiv.org/abs/2610.22322v1","arxiv_id":"2610.22322v1","published":"2026-10-15T17:51:57Z"},{"title":"AVC-Reward: Event-Centric Audio-Video Consistency Evaluation","abstract":"As joint audio-video generation models become more capable, they can generate audio-visual scenes exhibiting three forms of complexity: multi-event composition, multi-layer audio mixture, and asymmetric cross-modal relations. However, current evaluations largely reuse metrics from earlier simple and unidirectional tasks. They typically evaluate an audio-video clip as a whole and rely on simple predefined relations, m","authors":["Yuruo Wu","Kaihang Pan","Kai Shen","Jiacheng Li","Shuyuan Tu","Jisheng Dang"],"url":"https://arxiv.org/abs/2610.22321v1","arxiv_id":"2610.22321v1","published":"2026-10-15T17:51:56Z"},{"title":"A team of Gemini 4 Argon agents beats Minecraft","abstract":"We placed four Gemini 4 Argon agents in a sandboxed Minecraft survival world and gave them one goal: defeat the Ender Dragon. Each agent controlled a player via an open-source library called Mineflayer and coordinated with its teammates through in-game chat and shared memory. No one assigned roles or wrote game logic. The agents planned, wrote, and debugged every program themselves. Researchers could watch the run on","authors":["Maxence Faldor","Satinder Singh","Roberta Raileanu"],"url":"https://arxiv.org/abs/2610.22319v1","arxiv_id":"2610.22319v1","published":"2026-10-15T17:51:32Z"},{"title":"Learning to Steer LLM Agents and Multi-Agent Systems","abstract":"Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration. However, these systems are prone to deviating from the overall goal as execution unfolds, which wastes execution resources and leads to undesired outcomes, a failure termed goal drift. In this work, we propose a steering agent that supervises execution on the f","authors":["Leyang Shen","Yaxin Gong","Yang Zhang","Chenxiao Fan","Jiakai Wang","Dong Wang"],"url":"https://arxiv.org/abs/2610.22316v1","arxiv_id":"2610.22316v1","published":"2026-10-15T17:51:17Z"},{"title":"Knowledge That Is Linear Before Fine-Tuning Generalizes After It","abstract":"Not all facts a language model can recall support further learning equally well. We distinguish active facts, which support generalization beyond their own recall, from inert facts, which can be recalled but do not support such generalization in a given task. We study this distinction by constructing new relations from existing ones. Take the relation that maps a place to its continent (Paris -> Europe) and assign","authors":["Faridoun Mehri","Hinrich Schütze"],"url":"https://arxiv.org/abs/2610.22300v1","arxiv_id":"2610.22300v1","published":"2026-10-15T17:47:52Z"},{"title":"AI Consilium: a Benchmark of LLM Clinical Multidisciplinary Second Opinions","abstract":"Large language models (LLMs) can generate differential diagnoses for complex cases, but a useful second opinion must also be factually grounded, clinically coherent, actionable, and safe. We developed a multidimensional benchmark using 300 diagnostically resolved NEJM--MedQA cases: 110 for physician reference ratings and Judge calibration and 190 for held-out evaluation. Eight single-model pipelines generated indepen","authors":["Alexey A. Bokov","Ivan I. Panin","Nikolay B. Nechaev","Mikhail Yu. Valkov","Valeria Yu. Chernina","Natalia Gombolevskaya"],"url":"https://arxiv.org/abs/2610.22298v1","arxiv_id":"2610.22298v1","published":"2026-10-15T17:46:37Z"},{"title":"Private Retrieval Augmented Generation via Random Projection","abstract":"Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it can also introduce privacy risks by leaking sensitive information from the retrieval datastore. We propose a simple method to preserve datastore privacy in RAG systems via random projection. By applying the same projection to both datastore embeddings and query embeddin","authors":["Dixi Yao","Tahseen Rabbani","Tian Li"],"url":"https://arxiv.org/abs/2610.22297v1","arxiv_id":"2610.22297v1","published":"2026-10-15T17:45:51Z"},{"title":"Towards Continual Learning: Mitigating Forgetting With Subspace Tuning","abstract":"Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to continual learning in LLMs. Recent works connect forgetting to how far, and along which directions, fine-tuning moves the weights. On-policy methods such as reinforcement learning and on-policy distillation avoid updating the principal singular d","authors":["Arnav Chavan","Kusha Sareen","Rishabh Tiwari","Abhranil Chandra","Johan Obando-Ceron","Kurt Keutzer"],"url":"https://arxiv.org/abs/2610.22293v1","arxiv_id":"2610.22293v1","published":"2026-10-15T17:44:43Z"},{"title":"EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search","abstract":"Reinforcement learning has become a common post-training technique to improve the accuracy of LLMs on verifiable tasks. However, such training can narrow the set of solutions the model can produce. Several approaches have attempted to address this behavior-sharpening effect, but they often optimize metrics that do not reflect the diversity of correct solutions. Ideally, we want a model that can produce multiple good ","authors":["Hanane Nour Moussa","Doeun Lee","Sachin Kumar"],"url":"https://arxiv.org/abs/2610.22290v1","arxiv_id":"2610.22290v1","published":"2026-10-15T17:43:31Z"},{"title":"Improving Environment-Agnostic Coding Agents: The Role of Scaffolding","abstract":"Stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We investigate why this advantage persists and whether an environment-agnostic agent can be improved without environment-specific engineering. We evaluate a Codex-based agent in two nearly identical prompt variants on ARC-AGI-3, AGI Maze, and Terminal-Bench. Each variant is d","authors":["Sergey Rodionov"],"url":"https://arxiv.org/abs/2610.22289v1","arxiv_id":"2610.22289v1","published":"2026-10-15T17:43:16Z"}],"history":{"events":[{"year":2018,"text":"Thirteen-year-old Jayme Closs was kidnapped from her home in Barron, Wisconsin, and held captive for 88 days.","url":"https://en.wikipedia.org/wiki/Kidnapping_of_Jayme_Closs"},{"year":2013,"text":"A 7.2-magnitude earthquake struck Bohol in the Philippines, resulting in 222 deaths.","url":"https://en.wikipedia.org/wiki/2013_Bohol_earthquake"},{"year":2011,"text":"Global demonstrations against economic inequality, corporate influence on government, and other issues, were held in more than 950 cities in 82 countries.","url":"https://en.wikipedia.org/wiki/15_October_2011_global_protests"},{"year":2007,"text":"New Zealand Police conducted several anti-terrorism raids in relation to the discovery of an alleged paramilitary training camp in the Urewera mountain ranges, arresting 17 people and seizing four guns and 230 rounds of ammunition.","url":"https://en.wikipedia.org/wiki/New_Zealand_Police"},{"year":2006,"text":"An earthquake registering 6.7 Mw occurred off the northwestern coast of the island of Hawaiʻi.","url":"https://en.wikipedia.org/wiki/2006_Kiholo_Bay_earthquake"},{"year":1996,"text":"The Irish Criminal Assets Bureau was established following the gangland murders of Detective Garda Jerry McCabe and investigative journalist Veronica Guerin.","url":"https://en.wikipedia.org/wiki/Criminal_Assets_Bureau"},{"year":1979,"text":"President Carlos Humberto Romero of El Salvador was overthrown and exiled in a military coup d'état.","url":"https://en.wikipedia.org/wiki/Carlos_Humberto_Romero"},{"year":1967,"text":"The Motherland Calls (depicted in coat of arms), a colossal statue in Volgograd, Russia, which commemorates the casualties of the Battle of Stalingrad, was dedicated, becoming the then-tallest statue in the world.","url":"https://en.wikipedia.org/wiki/The_Motherland_Calls"},{"year":1965,"text":"Vietnam War protests: At an anti-war rally in New York City, David J. Miller burned his draft card (example pictured), the first such act to result in arrest under a new amendment to the Selective Service Act.","url":"https://en.wikipedia.org/wiki/Opposition_to_United_States_involvement_in_the_Vietnam_War"},{"year":1954,"text":"Hurricane Hazel made landfall in the Carolinas in the United States before moving north to Toronto in Canada later the same day, killing 176 people in the two countries.","url":"https://en.wikipedia.org/wiki/Hurricane_Hazel"},{"year":1932,"text":"Air India (modern aircraft pictured), the flag carrier airline of India, began operations under the name Tata Airlines.","url":"https://en.wikipedia.org/wiki/Air_India"},{"year":1888,"text":"The \"From Hell\" letter, allegedly from Jack the Ripper, was sent to George Lusk, the chairman of the Whitechapel Vigilance Committee in London.","url":"https://en.wikipedia.org/wiki/From_Hell_letter"},{"year":1864,"text":"American Civil War: Confederate forces captured Glasgow, Missouri, although it had little long-term benefit as Price's Missouri Expedition was defeated a week later.","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1815,"text":"Napoleon began his exile on Saint Helena in the South Atlantic Ocean, which would encompass his final six years.","url":"https://en.wikipedia.org/wiki/Napoleon"},{"year":1529,"text":"Ottoman–Habsburg wars: The siege of Vienna ended with Austrian forces repelling the invading Turks, turning the tide against almost a century of conquest in Europe by the Ottoman Empire.","url":"https://en.wikipedia.org/wiki/Ottoman%E2%80%93Habsburg_wars"}],"births":[{"year":2005,"text":"Christian, Crown Prince of Denmark","url":"https://en.wikipedia.org/wiki/Christian%2C_Crown_Prince_of_Denmark"},{"year":2000,"text":"Melki Sedek Huang, Indonesian activist and sex offender","url":"https://en.wikipedia.org/wiki/Melki_Sedek_Huang"},{"year":1999,"text":"Bailee Madison, American-Canadian actress and singer","url":"https://en.wikipedia.org/wiki/Bailee_Madison"},{"year":1999,"text":"Ben Woodburn, Welsh footballer","url":"https://en.wikipedia.org/wiki/Ben_Woodburn"},{"year":1998,"text":"Teuku Wariza Aris Munandar, Indonesian activist and politician","url":"https://en.wikipedia.org/wiki/2023_Banda_Aceh_anti-Rohingya_protest"},{"year":1996,"text":"Charly Musonda, Belgian footballer","url":"https://en.wikipedia.org/wiki/Charly_Musonda_Jr."},{"year":1996,"text":"Grace Van Dien, American actress","url":"https://en.wikipedia.org/wiki/Grace_Van_Dien"},{"year":1996,"text":"Zelo, South Korean rapper and dancer","url":"https://en.wikipedia.org/wiki/Zelo"}],"deaths":[{"year":2025,"text":"Jim Bolger, New Zealand businessman and politician, 35th Prime Minister of New Zealand (born 1935)","url":"https://en.wikipedia.org/wiki/Jim_Bolger"},{"year":2024,"text":"Mike Jackson, English general (born 1944)","url":"https://en.wikipedia.org/wiki/Mike_Jackson_(British_Army_officer)"},{"year":2021,"text":"David Amess, British politician, member of Parliament for Southend West (born 1952)","url":"https://en.wikipedia.org/wiki/David_Amess"},{"year":2018,"text":"Paul Allen, co-founder of Microsoft, philanthropist, owner of the Seattle Seahawks (born 1953)","url":"https://en.wikipedia.org/wiki/Paul_Allen"},{"year":2017,"text":"Chinggoy Alonzo, Filipino theater, movie & television actor (born 1950)","url":"https://en.wikipedia.org/wiki/Chinggoy_Alonzo"},{"year":2017,"text":"Deng Ken, Chinese politician, younger brother of former Paramount leader of China Deng Xiaoping (born 1911)","url":"https://en.wikipedia.org/wiki/Deng_Ken"},{"year":2014,"text":"Giovanni Reale, Italian philosopher and historian (born 1931)","url":"https://en.wikipedia.org/wiki/Giovanni_Reale"},{"year":2013,"text":"Donald Bailey, American drummer (born 1933)","url":"https://en.wikipedia.org/wiki/Donald_Bailey_(musician)"}]},"census":{"panel_version":2,"probed":14770,"error_rate":19.16,"degraded":false,"rates":{"head":12.9,"tail":6.2,"ai_native":73.64}},"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."}