{"date":"2026-10-22","schema_version":1,"captured_at":"2026-10-23T09:34:58.628Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Jailer 2","views":278796,"rank":4,"extract":"Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar. It was produced by Kalanithi Maran through Sun Pictures. A sequel to Jailer (2023), the film stars Rajinikanth, Ramya Krishnan, Yogi Babu, Mirnaa and Rithvik","url":"https://en.wikipedia.org/wiki/Jailer_2","thumb":"https://upload.wikimedia.org/wikipedia/en/d/dc/Jailer_2_poster.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"PHP2Shell","views":238212,"rank":5,"extract":"PHP2Shell is a pair of high-impact vulnerabilities in PHP versions 5.4–8.5 that allow remote code execution (RCE). Their existence was publicly announced on 20 October 2026. 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ms","points":216,"comments":40,"url":"https://globus.software/blog/2026/10/16/cutting-map-polygons/","discussion":"https://news.ycombinator.com/item?id=50193170"}],"research":[{"title":"Direct Pass@$k$ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning","abstract":"Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution. Running all candidate solutions to completion can be computationally demanding, so recent work prunes the candidate pool after generating only a short prefix, committing further computation only to the surviving candidates. ","authors":["Mahdi Salmani","Adel Javanmard","Devansh Gupta","Meisam Razaviyayn"],"url":"https://arxiv.org/abs/2610.31667v1","arxiv_id":"2610.31667v1","published":"2026-10-22T17:59:48Z"},{"title":"Cost-Aware Abstention for LLM-Based IT Ticket Classification","abstract":"Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on calibration data under explicit costs, then evaluate that frozen policy once on held-out test data. On a public 47,837-ticket, eight-class corpus, we compare four trained non-LLM classifiers with four large language models u","authors":["Sachin Gupta"],"url":"https://arxiv.org/abs/2610.31657v1","arxiv_id":"2610.31657v1","published":"2026-10-22T17:58:52Z"},{"title":"METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning","abstract":"Meta-reinforcement learning aims to train agents that adapt quickly to unseen tasks from limited interaction, but this requires representing task evidence over long interaction histories. Classical recurrent agents compress experience into hidden states, while transformer-based agents retrieve from past trajectories at quadratic memory and computation cost. In this work, we propose Memory-Enhanced Test-Time Adaptatio","authors":["Ali Najar","Hosein Hasani","Amir Kooshan Fattah","Yasamin Rajabi","Mahdieh Soleymani Baghshah"],"url":"https://arxiv.org/abs/2610.31656v1","arxiv_id":"2610.31656v1","published":"2026-10-22T17:58:27Z"},{"title":"EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence","abstract":"Tool-using language model agents rely on environmental facts to decide whether an action is permitted. When a tool response omits a safety-relevant fact, an agent may carry out a prohibited action even though the user request is benign and every returned statement is true. We introduce \\bench{}, an executable benchmark for evaluating unsafe execution, safety boundaries, and evidence recovery in LLM agents under missi","authors":["Qijin Zhang","Hanzhen Wang","Yuehan Zhao","Haoyu Dai","Yao Shu","Zhongxiang Dai"],"url":"https://arxiv.org/abs/2610.31650v1","arxiv_id":"2610.31650v1","published":"2026-10-22T17:57:04Z"},{"title":"Low-Bit N-Gram Embeddings for Language Models","abstract":"Many recent open-weight language models use n-gram embedding tables to improve performance. These tables store learned representations of short token sequences, such as pairs or triples of consecutive tokens, which the model retrieves during processing. However, they can require substantial memory: DeepSeek-V4.1-Flash, for example, allocates 196 billion parameters to its Engram n-gram memory. We therefore investigate","authors":["Stephen Chung","Wenyu Du"],"url":"https://arxiv.org/abs/2610.31646v1","arxiv_id":"2610.31646v1","published":"2026-10-22T17:56:13Z"},{"title":"Causal Chain of Thought: Mitigating Attention Leakage via Context Isolation","abstract":"Chain-of-thought (CoT) prompting elicits step-by-step rationales that appear to explain model predictions, but there is no guarantee these steps causally drive the final answer. In standard autoregressive generation, dense attention allows each reasoning step to attend to the full context, enabling the model to derive answers through direct lookup while generating plausible-sounding but post-hoc justifications. We in","authors":["Gabriele Dominici","Teague McMillan","Fazl Barez","Marc Langheinrich","Martin Gjoreski"],"url":"https://arxiv.org/abs/2610.31639v1","arxiv_id":"2610.31639v1","published":"2026-10-22T17:52:56Z"},{"title":"Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3","abstract":"Instrumental convergence is often invoked as a general reason to expect capable agents to acquire or express power-seeking subgoals: self-preservation, resource acquisition, deception, or resistance to oversight which poses an important threat to the society. For LLMs, reinforcement learning is the first stage of post-training that directly optimizes behavior against an objective; therefore, it is the stage where con","authors":["Barış Alparslan","Sandy Tanwisuth","Caleb Petterson"],"url":"https://arxiv.org/abs/2610.31624v1","arxiv_id":"2610.31624v1","published":"2026-10-22T17:44:41Z"},{"title":"How Ground Truth Shapes Retrieval Scores: Evidence-Aware Evaluation of a Retrieval-Augmented Generation System for Breast Lesions","abstract":"Purpose: To describe an evidence-aware retrieval evaluation protocol for a clinical retrieval-augmented generation (RAG) system over breast lesion management literature, addressing the dependence of retrieval scores on annotation coverage. Methods: An 18-document corpus and an 18-query development benchmark, with one Yes/No or WH-question per document, were used. Retrieval combined dense embeddings, BM25, and cross-e","authors":["Miguel Mochizuki Silva","Samuel Ismael","Mateus C. Dias","Thaís Gaudêncio","Yuri Malheiros","Barufaldi"],"url":"https://arxiv.org/abs/2610.31606v1","arxiv_id":"2610.31606v1","published":"2026-10-22T17:36:14Z"},{"title":"Simulated Control Evals","abstract":"Control evaluations measure whether safeguards can prevent an AI model from causing undesired outcomes even when it is intentionally trying to do so. These evaluations typically use purpose-built control settings and extrapolate their results to a model's deployment. However, the effectiveness of safeguards in deployment can depend on features of environments and tasks that control settings do not represent. To broad","authors":["Jeffrey Olmo","David Zhang","Tyler Tracy"],"url":"https://arxiv.org/abs/2610.31593v1","arxiv_id":"2610.31593v1","published":"2026-10-22T17:26:42Z"},{"title":"VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act","abstract":"Institutions that deploy large language models in regulated settings often need technical evidence of responsible AI practices before approving a deployment. COMPL-AI provides a technical interpretation of the EU AI Act that organises this evidence under six principle groups and eighteen technical requirements, twelve of which current benchmarks can measure. The benchmark suites that produce this evidence are typical","authors":["Robin Quériaux","Solveig Bachellery","Léa Déléris","Saurabhsingh Rajput","Mariam Barry","Tushar Sharma"],"url":"https://arxiv.org/abs/2610.31588v1","arxiv_id":"2610.31588v1","published":"2026-10-22T17:23:39Z"},{"title":"Would an LLM Match into a Residency? Uncertainty, Reasoning and Image Use of Open Language Models on the Spanish MIR Examination","abstract":"Accuracy on medical examinations is common evidence offered for the clinical use of language models, yet a health system that intends to run an open model on its own hardware needs to know considerably more: whether the model recognizes when it is likely to be wrong, whether medical fine-tuning improves this ability, whether its correct answers rest on sound clinical reasoning, or whether it actually uses the images ","authors":["Miguel Fernandez-de-Retana","Unai Zulaika","Ruben Sanchez-Corcuera","Aritz Bilbao-Jayo","Aitor Almeida"],"url":"https://arxiv.org/abs/2610.31586v1","arxiv_id":"2610.31586v1","published":"2026-10-22T17:23:11Z"},{"title":"Who Is Sycophantic to Whom? Confident LLMs, Human Deference, and the Collapse of Trust Calibration","abstract":"Research on sycophancy asks whether large language models (LLMs) bend towards what their users want to hear. We ask the reverse question: when an LLM is confident, consistent, and wrong, do users bend towards it? In a within-subjects experiment, 32 participants learned up to five science topics by chatting with five assistants built on the same model (GPT-5-mini). Unknown to participants, each assistant's briefing ha","authors":["Deepthika S","Pawni Yadav","B. Sankar"],"url":"https://arxiv.org/abs/2610.31579v1","arxiv_id":"2610.31579v1","published":"2026-10-22T17:15:59Z"},{"title":"What Makes a Good Unmasking Policy? A Controlled Study of Learned Unmasking in Diffusion Language Models","abstract":"Masked diffusion language models generate tokens in parallel, but their efficiency depends on which predictions are committed. Learned unmasking policies improve the speed--accuracy trade-off, yet differ in supervision, inputs, and capacity, obscuring which choices matter. We view supervised policies as distilling a decoding procedure through a teacher and examine independently what the teacher labels as committable,","authors":["Jinya Sakurai","Xun Xu","Jaehong Yoon"],"url":"https://arxiv.org/abs/2610.31576v1","arxiv_id":"2610.31576v1","published":"2026-10-22T17:15:04Z"},{"title":"Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure","abstract":"In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured information from unstructured content and deduce attributes users never disclosed. A person's true exposure is not what they post but what can be inferred from it. As these practices become routine, exposure to social engineering attacks becomes an operational certainty, demandin","authors":["Pellegrino Casoria","Simon Pietro Romano"],"url":"https://arxiv.org/abs/2610.31573v1","arxiv_id":"2610.31573v1","published":"2026-10-22T17:13:46Z"},{"title":"When to Follow the Teacher: Advantage-Based On-Policy Distillation","abstract":"Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student. However, standard OPD trains the student to follow the teacher at every state of the trajectory, assuming that teacher guidance is always beneficial. We instead distinguish between what the teacher prefers and what actually helps the current student suc","authors":["Mahdi Sabbaghi","George Pappas","Hamed Hassani","Adel Javanmard"],"url":"https://arxiv.org/abs/2610.31563v1","arxiv_id":"2610.31563v1","published":"2026-10-22T17:05:21Z"},{"title":"Contestable Agents for Data Governance: Recording Human Stewardship of AI-Generated Metadata Across Structured and Unstructured Systems","abstract":"Industrial organizations hold operational knowledge in structured records, such as telemetry, point lists, and transactions, and in unstructured sources, such as manuals, service notes, communications, and inspection reports. Governance programs often manage these sources separately. Classifying, linking, and documenting them depends on a small group of employees with deep domain knowledge. Agents built on large lang","authors":["Humza Akhtar","Brian Freeman"],"url":"https://arxiv.org/abs/2610.31553v1","arxiv_id":"2610.31553v1","published":"2026-10-22T16:57:01Z"},{"title":"Beyond Causal Masks: Auditing and Localizing Hidden Future-Token Leakage in Adaptive Sequence Models","abstract":"Hidden future-token leakage can create false architectural gains: an autoregressive model may appear substantially better under teacher-forced evaluation while its free-running generation deteriorates. We investigate this failure in an adaptive residual-routing decoder whose sequence-level routing summary exposes prefix positions to future tokens despite a correct causal attention mask. An anomalous 24-layer checkpoi","authors":["Huiting Liu","Jinsong Wu"],"url":"https://arxiv.org/abs/2610.31522v1","arxiv_id":"2610.31522v1","published":"2026-10-22T16:34:32Z"},{"title":"Jev-DLC: Decision-Level Continual Learning from Typed Feedback","abstract":"Individual preferences often become visible only after deployment, when a user accepts one decision or revokes it in favor of another. Jev-style typed interfaces make this feedback explicit by identifying the selected action and, under revocation, the decision it replaces. We study how these consequences persist across requests as Decision-Level Continual Learning from Typed Feedback (Jev-DLC). Jev-DLC treats each co","authors":["Yangyang Feng","Jianle Gan","Zhuoyan Feng","Chengwei Qin"],"url":"https://arxiv.org/abs/2610.31521v1","arxiv_id":"2610.31521v1","published":"2026-10-22T16:33:30Z"},{"title":"The Geometry of Metaphor: Probing and Steering Metaphor Representations in Large Language Models","abstract":"Metaphor is a crucial aspect of human cognition. As such, we would like to understand how it is represented in large language models (LLMs) and how it might be controlled. We use mechanistic interpretability techniques to identify causal subspaces in a range of LLMs covering different architectures and sizes. We show that we are able to steer output on classification tasks across different parts of speech. We are als","authors":["Shihui Li","Mette Andersen","Martha Lewis"],"url":"https://arxiv.org/abs/2610.31520v1","arxiv_id":"2610.31520v1","published":"2026-10-22T16:33:01Z"},{"title":"From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims","abstract":"Large language models (LLMs) are increasingly used to generate domain-specific commercial & medical content (e.g., claims, statements, and promotional phrases) in regulated industries such as pharmaceuticals. However, ensuring its regulatory compliance, factual accuracy, and audience-specific personalization remains a major challenge. We report a production case study asking whether moving compliance and personal","authors":["Suyash Mishra","Qiang Li","Subhabrata Adak","Upender Phogat","Srikanth Patil","Satyanarayan Pati"],"url":"https://arxiv.org/abs/2610.31519v1","arxiv_id":"2610.31519v1","published":"2026-10-22T16:31:36Z"},{"title":"DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination","abstract":"Coordinating two players in a cooking task requires a vision-language agent to track changing scene states, sequence recipe steps, and allocate shared resources under a time budget. We introduce an Overcooked-inspired benchmark for evaluating these capabilities in Unreal Engine environments constructed using coding agents. A single vision-language model controls both players from top-down visual observations, selecti","authors":["Ziyi Jiao","Heng Zhang","Drishti Regmi","Xiaokang Ye","Lianhui Qin","Peter Yichen Chen"],"url":"https://arxiv.org/abs/2610.31511v1","arxiv_id":"2610.31511v1","published":"2026-10-22T16:26:38Z"},{"title":"FairFRESH: A Neuro-Symbolic Framework for Fair and Explainable Household Food Recommendation","abstract":"Recommender systems typically serve one user at a time. However, in households, members share a finite pantry and follow different dietary rules, making independent recommendations prone to resource conflicts and dietary violations. We formulate household food recommendation as constrained fair division and present FairFRESH, a neuro-symbolic framework that learns per-member utilities, allocates foods under explicit ","authors":["Kodur Sai Vinay Sathvik","Kakoli Bora","Tejaswini R. M"],"url":"https://arxiv.org/abs/2610.31506v1","arxiv_id":"2610.31506v1","published":"2026-10-22T16:23:04Z"},{"title":"SABLE: Scalable Affine Bayesian Latent-space Exploration","abstract":"Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations are expensive. Generative models provide continuous latent representations, but their latent spaces can have thousands to tens of thousands of dimensions, creating a regime where the latent dimension $D$ greatly exceeds the evaluation budget.","authors":["Erik Bodin","Lisa Maria Kreusser","Donney Fan","Henry Moss","Geoff Pleiss","Nicolas Durrande"],"url":"https://arxiv.org/abs/2610.31495v1","arxiv_id":"2610.31495v1","published":"2026-10-22T16:13:42Z"},{"title":"Training Small Language Models for Transferable Tool Selection","abstract":"As LLM agents operate over corpora of more than ten thousand tools, selecting the right tool for a complex multi-step user instruction becomes a central bottleneck. Tool descriptions cannot all fit in context, and relevance-based retrievers only rank tools without knowing which are actually needed. Recent work instead leverages proprietary commercial LLMs like Claude or ChatGPT to drive a retriever, decomposing the i","authors":["Zhaoyi Zhou","Akshayaa Magesh","Jeongyeol Kwon","Artem Kuriksha","Daniel Jiang","Andrea Zanette"],"url":"https://arxiv.org/abs/2610.31483v1","arxiv_id":"2610.31483v1","published":"2026-10-22T16:07:49Z"},{"title":"MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models","abstract":"Vision-Language Models (VLMs) have shown strong potential for video anomaly detection, but dense frame-level inference makes them impractical for real-time deployment. Existing acceleration commonly relies on uniform sampling, which ignores content dynamics and can either waste computation on static segments or miss localized anomalies. We propose MoG-VLM a motion-adaptive anomaly detection framework that leverages m","authors":["Khaled Waleed Dawoud","Muhammad Zaigham Zaheer","Khan Muhammad","Muhammad Haris Khan"],"url":"https://arxiv.org/abs/2610.31478v1","arxiv_id":"2610.31478v1","published":"2026-10-22T16:03:47Z"}],"history":{"events":[{"year":2015,"text":"A sword-wielding man attacked students and teachers at a high school in Trollhättan, killing three people in Sweden's deadliest school attack.","url":"https://en.wikipedia.org/wiki/2015_Trollh%C3%A4ttan_school_attack"},{"year":2014,"text":"In Ottawa, Canada, the downtown core was placed on lockdown after a series of shootings at Parliament Hill.","url":"https://en.wikipedia.org/wiki/Ottawa"},{"year":2005,"text":"Bellview Airlines Flight 210 crashed in Ogun State, Nigeria, killing all 117 people on board.","url":"https://en.wikipedia.org/wiki/Bellview_Airlines_Flight_210"},{"year":1989,"text":"TAN-SAHSA Flight 414 crashed in approach to Toncontín International Airport, killing 131 out of 146 people onboard.","url":"https://en.wikipedia.org/wiki/TAN-SAHSA_Flight_414"},{"year":1987,"text":"John Adams' (pictured) opera Nixon in China premiered.","url":"https://en.wikipedia.org/wiki/John_Adams_(composer)"},{"year":1966,"text":"With their album The Supremes A' Go-Go, the Supremes became the first all-female group to reach number one on the Billboard 200 chart.","url":"https://en.wikipedia.org/wiki/The_Supremes_A'_Go-Go"},{"year":1964,"text":"The first volume of Ian Fleming's children's novel Chitty-Chitty-Bang-Bang was published posthumously.","url":"https://en.wikipedia.org/wiki/Ian_Fleming"},{"year":1945,"text":"Workers at the Cigar Factory in Charleston, South Carolina, began a labor strike against the American Tobacco Company that lasted more than five months.","url":"https://en.wikipedia.org/wiki/Cigar_Factory"},{"year":1940,"text":"After evading Vichy French and Francoist Spanish authorities, Belgian prime minister Hubert Pierlot arrived in London, marking the beginning of the Belgian government in exile.","url":"https://en.wikipedia.org/wiki/Vichy_France"},{"year":1936,"text":"The Royal Navy cutlass was withdrawn from combat service.","url":"https://en.wikipedia.org/wiki/Royal_Navy_cutlasses"},{"year":1936,"text":"Dod Orsborne, captain of the fishing trawler Girl Pat, was convicted of its theft and imprisoned, having caused a media sensation when it went missing.","url":"https://en.wikipedia.org/wiki/Dod_Orsborne"},{"year":1924,"text":"The educational non-profit organization Toastmasters International was founded at a YMCA in Santa Ana, California.","url":"https://en.wikipedia.org/wiki/Toastmasters_International"},{"year":1907,"text":"A bank run forced New York's Knickerbocker Trust Company to suspend operations, triggering the Panic of 1907 (pictured).","url":"https://en.wikipedia.org/wiki/Bank_run"},{"year":1895,"text":"At Gare Montparnasse in Paris, an express train derailed after overrunning the buffer stop and crashed through the station wall (pictured).","url":"https://en.wikipedia.org/wiki/Gare_Montparnasse"},{"year":1877,"text":"Scotland's worst mining accident occurred when an explosion at a colliery in Blantyre, South Lanarkshire, killed 207 miners.","url":"https://en.wikipedia.org/wiki/Blantyre_mining_disaster"},{"year":1740,"text":"A two-week massacre of ethnic Chinese in Batavia, Dutch East Indies, came to an end with at least 10,000 people killed.","url":"https://en.wikipedia.org/wiki/1740_Batavia_massacre"},{"year":1727,"text":"George II and Caroline of Ansbach (pictured) were crowned king and queen of Great Britain in Westminster Abbey.","url":"https://en.wikipedia.org/wiki/George_II_of_Great_Britain"},{"year":1724,"text":"J. S. Bach led the first performance of the chorale cantata Schmücke dich, o liebe Seele (Adorn yourself, O dear soul), based on the communion hymn of the same name, in Leipzig.","url":"https://en.wikipedia.org/wiki/Johann_Sebastian_Bach"}],"births":[{"year":2001,"text":"Brian Branch, American football player","url":"https://en.wikipedia.org/wiki/Brian_Branch"},{"year":2001,"text":"Jo Yu-ri, South Korean singer and actress","url":"https://en.wikipedia.org/wiki/Jo_Yuri"},{"year":2000,"text":"Baby Keem, American rapper and record producer","url":"https://en.wikipedia.org/wiki/Baby_Keem"},{"year":1999,"text":"Geraldo Perdomo, Dominican baseball player","url":"https://en.wikipedia.org/wiki/Geraldo_Perdomo"},{"year":1998,"text":"Roddy Ricch, American rapper","url":"https://en.wikipedia.org/wiki/Roddy_Ricch"},{"year":1997,"text":"Jan Köstering, German politician","url":"https://en.wikipedia.org/wiki/Jan_K%C3%B6stering"},{"year":1996,"text":"B.I, South Korean rapper, singer-songwriter and producer","url":"https://en.wikipedia.org/wiki/B.I_(rapper)"},{"year":1996,"text":"Johannes Høsflot Klæbo, Norwegian ski runner","url":"https://en.wikipedia.org/wiki/Johannes_H%C3%B8sflot_Kl%C3%A6bo"}],"deaths":[{"year":2025,"text":"David Ball, English electronic musician (born 1959)","url":"https://en.wikipedia.org/wiki/David_Ball_(electronic_musician)"},{"year":2024,"text":"Richard A. Cash, American global health researcher (born 1941)","url":"https://en.wikipedia.org/wiki/Richard_A._Cash"},{"year":2024,"text":"Grizzly 399, American grizzly bear (born 1996)","url":"https://en.wikipedia.org/wiki/Grizzly_399"},{"year":2024,"text":"Gustavo Gutiérrez, Peruvian philosopher, theologian and priest (born 1928)","url":"https://en.wikipedia.org/wiki/Gustavo_Guti%C3%A9rrez"},{"year":2024,"text":"Lynda Obst, American film producer and author (born 1950)","url":"https://en.wikipedia.org/wiki/Lynda_Obst"},{"year":2024,"text":"Fernando Valenzuela, Mexican baseball player, coach, and sportscaster (born 1960)","url":"https://en.wikipedia.org/wiki/Fernando_Valenzuela"},{"year":2021,"text":"Peter Scolari, American actor (born 1955)","url":"https://en.wikipedia.org/wiki/Peter_Scolari"},{"year":2017,"text":"George Young, Australian musician, songwriter and record producer (born 1946)","url":"https://en.wikipedia.org/wiki/George_Young_(rock_musician)"}]},"census":{"panel_version":2,"probed":14770,"error_rate":22,"degraded":false,"rates":{"head":13.6,"tail":5.7,"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."}