{"date":"2026-10-21","schema_version":1,"captured_at":"2026-10-22T09:40:22.896Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":13},"attention":[{"title":"Kim Jong Un","views":321739,"rank":4,"extract":"Kim Jong Un is a North Korean politician and dictator who has been serving as the supreme leader of North Korea since 2011, following the death of his father, Kim Jong Il. 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out","points":276,"comments":2,"url":"https://www.php.net/index.php","discussion":"https://news.ycombinator.com/item?id=50180943"},{"title":"Dutch National Cyber Security Center is Offline because it uses PHP","points":269,"comments":94,"url":"https://www.ncsc.nl/actueel","discussion":"https://news.ycombinator.com/item?id=50179828"},{"title":"Patch the Planet: global response coordination as agents uncover vulnerabilities","points":256,"comments":174,"url":"https://openai.com/index/coordinated-disclosure-of-critical-vulnerabilities/","discussion":"https://news.ycombinator.com/item?id=50181175"},{"title":"Qwen3-Coder writes less secure code when told it's for a US persona","points":240,"comments":117,"url":"https://www.hirundo.io/blog/qwen3-coder-vulnerabilities","discussion":"https://news.ycombinator.com/item?id=50179706"},{"title":"Meta hit with $25B penalty for deceiving users about privacy on Facebook","points":206,"comments":2,"url":"https://nmdoj.gov/press-release/the-new-mexico-department-of-justice-secures-another-landmark-victory-against-big-tech-with-historic-25-billion-penalty-against-facebook/","discussion":"https://news.ycombinator.com/item?id=50186215"}],"research":[{"title":"Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation","abstract":"Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shallow processing while ","authors":["Zhanshuo Cao","Jiakai Tang","Yuqi Zhou","Sunhao Dai","Jun Xu"],"url":"https://arxiv.org/abs/2610.30128v1","arxiv_id":"2610.30128v1","published":"2026-10-21T17:58:06Z"},{"title":"PhysiCalWorld: In-Context Physics Calibration for World Models","abstract":"Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics properties that determine transition dynamics, such as an object's mass distribution, joint damping, or surface friction. Without calibrating these physics factors for world models, predictions tend to regress toward an empirical prior and induce severe drift","authors":["Yunhao Luo","Wentao He","Yinpei Dai","Hyelin Nam","Hongze Fu","Joyce Chai"],"url":"https://arxiv.org/abs/2610.30125v1","arxiv_id":"2610.30125v1","published":"2026-10-21T17:57:59Z"},{"title":"Environment Duels: A Microcosm of LLM Co-Evolution","abstract":"Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a solver? We introduce Environment Duels, a dynamic agentic benchmark that evaluates both capabilities on interactive environments. Each model writes environments","authors":["Shibo Jin","Zhiqiu Xu","Yiling Zou","Shangyu Gong","Mayur Naik"],"url":"https://arxiv.org/abs/2610.30116v1","arxiv_id":"2610.30116v1","published":"2026-10-21T17:55:31Z"},{"title":"The End of Consensus Research?","abstract":"Consensus is a fundamental problem in distributed computing and at the core of state machine replication (SMR). Despite decades of active human research, discovering practical low-latency solutions to the problem is still open, as it requires combining protocol knowledge, correctness reasoning, implementation, and experimental evaluation. We report on how AI helped us discover A-greement, a new consensus protocol for","authors":["Clément Burgelin","Rachid Guerraoui","Zicong Hong","Yinan Hu","Anne-Marie Kermarrec","Rafael Pires"],"url":"https://arxiv.org/abs/2610.30114v1","arxiv_id":"2610.30114v1","published":"2026-10-21T17:55:19Z"},{"title":"Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research","abstract":"Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leaving open whether large language models (LLMs) can complete this loop and produce strategies that work beyond the research period. We introduce Quant-Arena, a be","authors":["Houde Qian","Zheng Jiang","Wenyi Li","Chenxi Xu","Xinjie Lin","Yuzhao Peng"],"url":"https://arxiv.org/abs/2610.30113v1","arxiv_id":"2610.30113v1","published":"2026-10-21T17:54:01Z"},{"title":"Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use","abstract":"Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensing and actuation under physical dynamics. We define multimodal embodied agents (MMEAs) as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from the resulting feedback. This raises a central questio","authors":["Yanzhe Chen","Ziyi Yang","Jifeng Zhu","Qiming Huang","Ruihe An","Peiyao Xu"],"url":"https://arxiv.org/abs/2610.30108v1","arxiv_id":"2610.30108v1","published":"2026-10-21T17:52:48Z"},{"title":"Asymmetric On-Policy Distillation Learns In-Context Exploration","abstract":"Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Learning such information-gathering behaviors tabula rasa via online reinforcement learning is sample-inefficient, whereas offline imitation averages expert actions across hidden states and fails to learn exploration. In this work, we propose ASTEROID, a","authors":["Sriyash Poddar","Yanda Bao","Jacob Krantz","Matthew Chang","Xavier Puig","Roozbeh Mottaghi"],"url":"https://arxiv.org/abs/2610.30105v1","arxiv_id":"2610.30105v1","published":"2026-10-21T17:50:31Z"},{"title":"BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard","abstract":"Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have made substantial progress, significant performance gaps under domain shift persist. To measure progress in this regard, we introduce BatchBench, a leaderboard t","authors":["Sonja Topf","Ana Sanchez-Fernandez","Günter Klambauer"],"url":"https://arxiv.org/abs/2610.30104v1","arxiv_id":"2610.30104v1","published":"2026-10-21T17:50:22Z"},{"title":"Differentially Private Sparse Reward Estimation with Preference Feedback","abstract":"Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typically assume dense reward parameterizations, resulting in estimation error bounds that scale polynom","authors":["Meng Ding","Mingxi Lei","Jie Zhang","Jinyan Liu","Di Wang"],"url":"https://arxiv.org/abs/2610.30099v1","arxiv_id":"2610.30099v1","published":"2026-10-21T17:45:42Z"},{"title":"AtomicRL: Atomic Reinforcement Learning for Long-Horizon Robotic Manipulation","abstract":"Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies. However, existing VLA-RL methods typically suffer from delayed and biased credit assignment due to trajectory-level sparse rewards, while imbalanced subtask mas","authors":["Likui Zhang","Zhe Zhu","Zhihao Zhan","Yibo Bai","Hanlin Wang","Dongwei Li"],"url":"https://arxiv.org/abs/2610.30097v1","arxiv_id":"2610.30097v1","published":"2026-10-21T17:45:24Z"},{"title":"Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization","abstract":"Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. Independent sets certify compatible edits whose verified gains, measured against the same current solution, add exactly. A coordinator constructs a safe compatible candidate and","authors":["Hanmin Li","Yury Demidovich","Peter Richtárik"],"url":"https://arxiv.org/abs/2610.30074v1","arxiv_id":"2610.30074v1","published":"2026-10-21T17:27:17Z"},{"title":"Charts as Epistemic Actions for Experience-Driven Agentic Data Analysis","abstract":"Data agents built on ReAct-style loops now execute complex analytical workflows over structured data, yet their intermediate reasoning still runs almost entirely through text. Human analysts instead move between numbers, tables, and charts, reading visual evidence to expose trends and anomalies that text summaries drop. We ask whether charts can serve as internal epistemic actions inside a data agent's reasoning loop","authors":["Xinzhe Wang","Xudong Weng","Zang Li","Shaolei Zhang"],"url":"https://arxiv.org/abs/2610.30072v1","arxiv_id":"2610.30072v1","published":"2026-10-21T17:26:53Z"},{"title":"From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks","abstract":"Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models for robot manipulation without task-specific training. However, their manipulation capabilities have not been systematically evaluated on standardized robotics benchmarks. In this report, we present a unified closed-loop ","authors":["May Espinola","Qifan Zhang","Yu Xiang"],"url":"https://arxiv.org/abs/2610.30051v1","arxiv_id":"2610.30051v1","published":"2026-10-21T17:11:52Z"},{"title":"SWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation","abstract":"Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a coding agent and evaluate whether the agent successfully solves the task. However, in real-world use cases, prompts are often not as well-formulated or structured as existing GitHub issues. Additionally, a coding agent session is not limited to a single prompt; it typic","authors":["Lorenz Brehme","Benjamin Steenhoek","Ruth Breu"],"url":"https://arxiv.org/abs/2610.30047v1","arxiv_id":"2610.30047v1","published":"2026-10-21T17:06:34Z"},{"title":"Which Differences Matter? Controlled Tests of Rule Transfer, Joint Reliability, and Information Use in Decision Models","abstract":"Decision models map stated rules and observed states to discrete answers. Aggregate accuracy leaves unresolved which rule and information changes guide those answers. We connect controlled input changes to transfer, joint reliability, and directed information use through executable arithmetic rules. Semantic rule separation and crossed rule and state tests make transfer and joint success separately measurable. We com","authors":["Tianyou Lai","Wentao Yue","Zhiwei Zuo","Qilei Li"],"url":"https://arxiv.org/abs/2610.30045v1","arxiv_id":"2610.30045v1","published":"2026-10-21T17:04:02Z"},{"title":"SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation","abstract":"Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action representation for both task intent and whole-body coordination. This may burden the high-level policy or leave one aspect underspecified. We present SP-HLM, a framework built on a sparse-pose-driven interface. The interface combines five task-critic","authors":["Zhaohui Yang","Quanbao Lin","Chengqi Shi","Zhenglang Weng","Jicheng Lin","Boyu Chen"],"url":"https://arxiv.org/abs/2610.30044v1","arxiv_id":"2610.30044v1","published":"2026-10-21T17:03:34Z"},{"title":"HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning","abstract":"Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs. Leading approaches such as the Darwin--Godel Machine and the Huxley--Godel Machine (HGM) rely on recursive self-improvement and were developed mainly for coding, where the competence needed to improve the agent coincides with the competence being impr","authors":["Sudipta Paul","Pengrui Quan","Aounon Kumar","Nicholas Tzou","Vivek Kulkarni","Srinivas Chappidi"],"url":"https://arxiv.org/abs/2610.30042v1","arxiv_id":"2610.30042v1","published":"2026-10-21T17:02:28Z"},{"title":"Robust Soft Skill Adaptation via Behavioral Localization in Tool-Using Agents","abstract":"Text skills can extend the capabilities of tool-using agents, but long skill descriptions increase computational costs. Soft prompts provide a compact alternative, yet learning from entire successful trajectories may also introduce unnecessary behavioral changes. We find that the useful effect of a text skill is highly concentrated in a small subset of trajectory positions. Based on this observation, we propose Behav","authors":["Zijian Jiang","Xilu Wang"],"url":"https://arxiv.org/abs/2610.30041v1","arxiv_id":"2610.30041v1","published":"2026-10-21T17:02:06Z"},{"title":"What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion","abstract":"Enterprise agents act on documents. Before an agent can follow a policy, answer a compliance question or file a ticket, some ingestion layer must turn a Word file or PDF into tokens - and that layer is where an agent deployed in the wild silently loses information. We show that one layer is lost by every mainstream converter - the meaning an author encodes in how text looks - and that the loss makes agents confidentl","authors":["Yikai Zhao","Saurabh Pandey","Pradeep Kumar Misra"],"url":"https://arxiv.org/abs/2610.30026v1","arxiv_id":"2610.30026v1","published":"2026-10-21T16:51:25Z"},{"title":"Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals","abstract":"Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities. We show that the Brillouin residual of a trained functional on the Kohn-Sham orbitals of an accurate density controls, to first order, both the self-consistent density shift and the gap between fitted and self-consistent energies. Penalizing it requires no self-consis","authors":["Yuhao Chen","Donald G. Truhlar","Xiao He"],"url":"https://arxiv.org/abs/2610.30022v1","arxiv_id":"2610.30022v1","published":"2026-10-21T16:49:03Z"},{"title":"GuideEvolve: Self-Evolving Agent Framework with Expert Guidance and 3Ts Mechanisms","abstract":"This study introduces GuideEvolve, a self-evolving agent framework incorporating expert guidance and three autonomous regulatory mechanisms, collectively termed the 3Ts: Think Fast and Slow, Throwback, and Termination by choice. The framework addresses critical limitations in extant self-evolving agent paradigms through dual innovations: (1) a task-specific expert-evaluation-driven evolutionary pipeline, and (2) the ","authors":["Meihong Jia","Yasemin Timar","Jonathan Taws"],"url":"https://arxiv.org/abs/2610.30017v1","arxiv_id":"2610.30017v1","published":"2026-10-21T16:46:22Z"},{"title":"Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories","abstract":"Understanding large, complex, and rapidly evolving code bases remains a core challenge for software engineering at scale, hindering onboarding, maintenance, refactoring, and impact analysis. Providing this information to a Large-Language Model (LLM) is another hurdle, as the LLM must understand not only the required code method but also its associated methods in the grand scheme of the repository. We propose Code Com","authors":["Sujoy Roychowdhury","Joseph H. R. Isaac","Jiangning Gao","Martin Klitte","Diarmuid Corcoran"],"url":"https://arxiv.org/abs/2610.30016v1","arxiv_id":"2610.30016v1","published":"2026-10-21T16:45:43Z"},{"title":"Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them","abstract":"LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference trajectories, without requiring new environment rollouts from the target agent. We derive error bounds relating squared coordinate distances to trajectory KL under explicit assumptions, and across eight cross-scored models the ","authors":["Shota Fujikawa","Takuya Tamura","Kosuke Akimoto","Daichi Haraguchi","Masafumi Oyamada"],"url":"https://arxiv.org/abs/2610.30014v1","arxiv_id":"2610.30014v1","published":"2026-10-21T16:44:02Z"},{"title":"Building Auditable AI Text Detectors with Sparse Autoencoders","abstract":"AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide margins. We present ","authors":["Liam Dugan","Callum McDougall","Matthew Denton","Philip Pham","Christine Kaeser-Chen","Chris Callison-Burch"],"url":"https://arxiv.org/abs/2610.30006v1","arxiv_id":"2610.30006v1","published":"2026-10-21T16:41:14Z"},{"title":"Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642","abstract":"Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real ba","authors":["Madhulatha Mandarapu","Sandeep Kunkunuru"],"url":"https://arxiv.org/abs/2610.30004v1","arxiv_id":"2610.30004v1","published":"2026-10-21T16:40:00Z"}],"history":{"events":[{"year":1994,"text":"North Korea and the United States signed the Agreed Framework to limit the former's nuclear weapons program and to normalize relations between the two countries.","url":"https://en.wikipedia.org/wiki/Agreed_Framework"},{"year":1994,"text":"In Seoul, South Korea, 32 people were killed and 17 others injured when a span of the Seongsu Bridge collapsed (pictured).","url":"https://en.wikipedia.org/wiki/Seoul"},{"year":1968,"text":"At the height of the Japanese university protests, protesters occupied Shinjuku Station in Tokyo and clashed violently with police.","url":"https://en.wikipedia.org/wiki/1968%E2%80%931969_Japanese_university_protests"},{"year":1966,"text":"A coal tip fell on the village of Aberfan, Wales, killing 144 people, mostly schoolchildren.","url":"https://en.wikipedia.org/wiki/Spoil_tip"},{"year":1959,"text":"The Solomon R. Guggenheim Museum, designed by American architect Frank Lloyd Wright, opened in New York City.","url":"https://en.wikipedia.org/wiki/Solomon_R._Guggenheim_Museum"},{"year":1944,"text":"World War II: The three-week-long Battle of Aachen concluded, making the city the first on German soil to be captured by the Allies.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1941,"text":"World War II: German soldiers massacred nearly 2,800 Serbs in Kragujevac in reprisal for insurgent attacks in the district of Gornji Milanovac.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1910,"text":"HMS Niobe arrived in Halifax Harbour to become the first large ship of the Royal Canadian Navy.","url":"https://en.wikipedia.org/wiki/HMS_Niobe_(1897)"},{"year":1867,"text":"The first and second of three treaties were signed near Medicine Lodge, Kansas, between the United States federal government and several Native American tribes in the Great Plains, requiring them to relocate to areas in present-day western Oklahoma.","url":"https://en.wikipedia.org/wiki/Medicine_Lodge_Treaty"},{"year":1858,"text":"French composer Jacques Offenbach's operetta Orpheus in the Underworld, featuring the music most associated with the can-can (audio featured), was first performed at the Théâtre des Bouffes-Parisiens in Paris.","url":"https://en.wikipedia.org/wiki/Jacques_Offenbach"},{"year":1854,"text":"Florence Nightingale and a staff of 38 nurses and 15 nuns were sent to the Ottoman Empire to help treat wounded British soldiers fighting in the Crimean War.","url":"https://en.wikipedia.org/wiki/Florence_Nightingale"},{"year":1345,"text":"Hundred Years' War: The English victory at the Battle of Auberoche marked a change in the military balance of power in Aquitaine, with the subsequent collapse of the French position.","url":"https://en.wikipedia.org/wiki/Hundred_Years'_War"},{"year":1096,"text":"First Crusade: At the Battle of Civetot, the Seljuk forces of Kilij Arslan destroyed the army of the People's Crusade as it marched toward Nicaea.","url":"https://en.wikipedia.org/wiki/First_Crusade"}],"births":[{"year":2002,"text":"Thierno Barry, French footballer","url":"https://en.wikipedia.org/wiki/Thierno_Barry_(footballer%2C_born_2002)"},{"year":1999,"text":"Matteo Gabbia, Italian footballer","url":"https://en.wikipedia.org/wiki/Matteo_Gabbia"},{"year":1998,"text":"Kerem Aktürkoğlu, Turkish footballer","url":"https://en.wikipedia.org/wiki/Kerem_Akt%C3%BCrko%C4%9Flu"},{"year":1995,"text":"Cameron Burgess, Scottish-Australian footballer","url":"https://en.wikipedia.org/wiki/Cameron_Burgess"},{"year":1995,"text":"Doja Cat, American rapper, singer and songwriter","url":"https://en.wikipedia.org/wiki/Doja_Cat"},{"year":1995,"text":"Antoinette Guedia Mouafo, Cameroonian swimmer","url":"https://en.wikipedia.org/wiki/Antoinette_Guedia_Mouafo"},{"year":1993,"text":"Kane Brown, American singer and songwriter","url":"https://en.wikipedia.org/wiki/Kane_Brown"},{"year":1992,"text":"Natasha Bassett, Australian actress","url":"https://en.wikipedia.org/wiki/Natasha_Bassett"}],"deaths":[{"year":2025,"text":"Francisco Pinto Balsemão, Portuguese politician, former Prime Minister of Portugal (born 1937)","url":"https://en.wikipedia.org/wiki/Francisco_Pinto_Balsem%C3%A3o"},{"year":2024,"text":"Mimi Hines, Canadian singer and comedian (born 1933)","url":"https://en.wikipedia.org/wiki/Mimi_Hines"},{"year":2023,"text":"Bobby Charlton, English footballer and manager (born 1937)","url":"https://en.wikipedia.org/wiki/Bobby_Charlton"},{"year":2023,"text":"Bobi, Portuguese dog (born 1992)","url":"https://en.wikipedia.org/wiki/Bobi_(dog)"},{"year":2023,"text":"Bill Hayden, Australian politician, 21st Governor General of Australia (born 1933)","url":"https://en.wikipedia.org/wiki/Bill_Hayden"},{"year":2021,"text":"Bernard Haitink, Dutch conductor and violinist (born 1929)","url":"https://en.wikipedia.org/wiki/Bernard_Haitink"},{"year":2020,"text":"Frank Bough, English television presenter (born 1933)","url":"https://en.wikipedia.org/wiki/Frank_Bough"},{"year":2015,"text":"France Bučar, Slovenian lawyer and politician (born 1923)","url":"https://en.wikipedia.org/wiki/France_Bu%C4%8Dar"}]},"census":{"panel_version":2,"probed":14770,"error_rate":25.37,"degraded":false,"rates":{"head":13.5,"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 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Every item links to its source."}