{"date":"2025-11-17","schema_version":1,"captured_at":"2026-08-25T22:56:16.569Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":14},"attention":[{"title":"Troy Parrott","views":364546,"rank":3,"extract":"Troy Daniel Parrott is an Irish professional footballer who plays as a striker for La Liga club Real Betis and the Republic of Ireland national team.","url":"https://en.wikipedia.org/wiki/Troy_Parrott","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/3/3b/Troy_Parrott_2025.png/330px-Troy_Parrott_2025.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"2026 FIFA World Cup qualification (UEFA)","views":341058,"rank":4,"extract":"The European section of the 2026 FIFA World Cup qualification competition acted as qualifiers for the 2026 FIFA World Cup, that was held in Canada, Mexico and the United States, for national teams that were members of the Union of European Football Association","url":"https://en.wikipedia.org/wiki/2026_FIFA_World_Cup_qualification_(UEFA)","thumb":null},{"title":"UFC 322","views":316030,"rank":5,"extract":"UFC 322: Della Maddalena vs. Makhachev was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on November 15, 2025, at Madison Square Garden in New York City, New York, United States.","url":"https://en.wikipedia.org/wiki/UFC_322","thumb":"https://upload.wikimedia.org/wikipedia/en/a/a9/UFC_322_poster.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"Google Chrome","views":282558,"rank":6,"extract":"Google Chrome is a cross-platform web browser developed by Google. 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While existing benchmarks like LoCoBench~\\cite{qiu2025locobench} assess long-context code understanding, they focus on single-turn evaluation and cannot capture the multi-turn interactive nature, tool usage patterns, and adaptive reasonin","authors":["Jielin Qiu","Zuxin Liu","Zhiwei Liu","Rithesh Murthy","Jianguo Zhang","Haolin Chen"],"url":"http://arxiv.org/abs/2511.13998v1","arxiv_id":"2511.13998v1","published":"2025-11-17T23:57:24Z"},{"title":"Artificial Intelligence Agents in Music Analysis: An Integrative Perspective Based on Two Use Cases","abstract":"This paper presents an integrative review and experimental validation of artificial intelligence (AI) agents applied to music analysis and education. We synthesize the historical evolution from rule-based models to contemporary approaches involving deep learning, multi-agent architectures, and retrieval-augmented generation (RAG) frameworks. The pedagogical implications are evaluated through a dual-case methodology: ","authors":["Antonio Manuel Martínez-Heredia","Dolores Godrid Rodríguez","Andrés Ortiz García"],"url":"http://arxiv.org/abs/2511.13987v1","arxiv_id":"2511.13987v1","published":"2025-11-17T23:46:47Z"},{"title":"Detecting and Steering LLMs' Empathy in Action","abstract":"We investigate empathy-in-action -- the willingness to sacrifice task efficiency to address human needs -- as a linear direction in LLM activation space. Using contrastive prompts grounded in the Empathy-in-Action (EIA) benchmark, we test detection and steering across Phi-3-mini-4k (3.8B), Qwen2.5-7B (safety-trained), and Dolphin-Llama-3.1-8B (uncensored). Detection: All models show AUROC 0.996-1.00 at optimal layers","authors":["Juan P. Cadile"],"url":"http://arxiv.org/abs/2511.16699v1","arxiv_id":"2511.16699v1","published":"2025-11-17T23:45:26Z"},{"title":"Node-Level Uncertainty Estimation in LLM-Generated SQL","abstract":"We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query's abstract syntax tree (AST). Our approach proceeds in two stages. First, we introduce a semantically aware labeling algorithm that, given a generated SQL and a gold reference, assigns node-level correctness without over-penalizing structural containers or alias variation.","authors":["Hilaf Hasson","Ruocheng Guo"],"url":"http://arxiv.org/abs/2511.13984v2","arxiv_id":"2511.13984v2","published":"2025-11-17T23:31:45Z"},{"title":"Data Whitening Improves Sparse Autoencoder Learning","abstract":"Sparse autoencoders (SAEs) have emerged as a promising approach for learning interpretable features from neural network activations. However, the optimization landscape for SAE training can be challenging due to correlations in the input data. We demonstrate that applying PCA Whitening to input activations -- a standard preprocessing technique in classical sparse coding -- improves SAE performance across multiple met","authors":["Ashwin Saraswatula","David Klindt"],"url":"http://arxiv.org/abs/2511.13981v1","arxiv_id":"2511.13981v1","published":"2025-11-17T23:20:58Z"},{"title":"Scene Graph-Guided Generative AI Framework for Synthesizing and Evaluating Industrial Hazard Scenarios","abstract":"Training vision models to detect workplace hazards accurately requires realistic images of unsafe conditions that could lead to accidents. However, acquiring such datasets is difficult because capturing accident-triggering scenarios as they occur is nearly impossible. To overcome this limitation, this study presents a novel scene graph-guided generative AI framework that synthesizes photorealistic images of hazardous","authors":["Sanjay Acharjee","Abir Khan Ratul","Diego Patino","Md Nazmus Sakib"],"url":"http://arxiv.org/abs/2511.13970v1","arxiv_id":"2511.13970v1","published":"2025-11-17T22:58:27Z"},{"title":"TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation","abstract":"The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Language Models (LLMs) show promise, existing approaches fall short in handling complex queries, visual information, and document interdependencies. We present TelcoAI, an ","authors":["Rahul Ghosh","Chun-Hao Liu","Gaurav Rele","Vidya Sagar Ravipati","Hazar Aouad"],"url":"http://arxiv.org/abs/2601.16984v1","arxiv_id":"2601.16984v1","published":"2025-11-17T22:55:45Z"},{"title":"Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge","abstract":"Reinforcement Learning (RL) faces significant challenges in adaptive healthcare interventions, such as dementia care, where data is scarce, decisions require interpretability, and underlying patient-state dynamic are complex and causal in nature. In this work, we present a novel framework called Causal structure-aware Reinforcement Learning (CRL) that explicitly integrates causal discovery and reasoning into policy o","authors":["Wenzheng Zhao","Ran Zhang","Ruth Palan Lopez","Shu-Fen Wung","Fengpei Yuan"],"url":"http://arxiv.org/abs/2512.00048v1","arxiv_id":"2512.00048v1","published":"2025-11-17T22:38:03Z"},{"title":"CORGI: Efficient Pattern Matching With Quadratic Guarantees","abstract":"Rule-based systems must solve complex matching problems within tight time constraints to be effective in real-time applications, such as planning and reactive control for AI agents, as well as low-latency relational database querying. Pattern-matching systems can encounter issues where exponential time and space are required to find matches for rules with many underconstrained variables, or which produce combinatoria","authors":["Daniel Weitekamp"],"url":"http://arxiv.org/abs/2511.13942v1","arxiv_id":"2511.13942v1","published":"2025-11-17T21:49:39Z"},{"title":"Preference-Based Learning in Audio Applications: A Systematic Analysis","abstract":"Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications. Through a PRISMA-guided systematic review of approximately 500 papers, we find that only 30 (6%) apply preference learning to audio tasks. Our analysis reveals a field in transition: pre-2021 works focused on emotion recognition using trad","authors":["Aaron Broukhim","Yiran Shen","Prithviraj Ammanabrolu","Nadir Weibel"],"url":"http://arxiv.org/abs/2511.13936v1","arxiv_id":"2511.13936v1","published":"2025-11-17T21:42:01Z"},{"title":"Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach","abstract":"Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation framework. Most existing selection bias metrics require answer ","authors":["Blessed Guda","Lawrence Francis","Gabrial Zencha Ashungafac","Carlee Joe-Wong","Moise Busogi"],"url":"http://arxiv.org/abs/2511.21709v1","arxiv_id":"2511.21709v1","published":"2025-11-17T21:31:37Z"},{"title":"Compute-in-Memory Implementation of State Space Models for Event Sequence Processing","abstract":"State space models (SSMs) have recently emerged as a powerful framework for long sequence processing, outperforming traditional methods on diverse benchmarks. Fundamentally, SSMs can generalize both recurrent and convolutional networks and have been shown to even capture key functions of biological systems. Here we report an approach to implement SSMs in energy-efficient compute-in-memory (CIM) hardware to achieve re","authors":["Xiaoyu Zhang","Mingtao Hu","Sen Lu","Soohyeon Kim","Eric Yeu-Jer Lee","Yuyang Liu"],"url":"http://arxiv.org/abs/2511.13912v2","arxiv_id":"2511.13912v2","published":"2025-11-17T21:06:52Z"},{"title":"What Works for 'Lost-in-the-Middle' in LLMs? A Study on GM-Extract and Mitigations","abstract":"The diminishing ability of large language models (LLMs) to effectively utilize long-range context-the \"lost-in-the-middle\" phenomenon-poses a significant challenge in retrieval-based LLM applications. To study the impact of this phenomenon in a real-world application setting, we introduce GM-Extract, a novel benchmark dataset meticulously designed to evaluate LLM performance on retrieval of control variables. To accu","authors":["Mihir Gupte","Eshan Dixit","Muhammad Tayyab","Arun Adiththan"],"url":"http://arxiv.org/abs/2511.13900v1","arxiv_id":"2511.13900v1","published":"2025-11-17T20:50:50Z"},{"title":"Jailbreaking Large Vision Language Models in Intelligent Transportation Systems","abstract":"Large Vision Language Models (LVLMs) demonstrate strong capabilities in multimodal reasoning and many real-world applications, such as visual question answering. However, LVLMs are highly vulnerable to jailbreaking attacks. This paper systematically analyzes the vulnerabilities of LVLMs integrated in Intelligent Transportation Systems (ITS) under carefully crafted jailbreaking attacks. First, we carefully construct a","authors":["Badhan Chandra Das","Md Tasnim Jawad","Md Jueal Mia","M. Hadi Amini","Yanzhao Wu"],"url":"http://arxiv.org/abs/2511.13892v1","arxiv_id":"2511.13892v1","published":"2025-11-17T20:29:48Z"},{"title":"The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing","abstract":"Artificial intelligence is accelerating a new era of food innovation, connecting data from farm to consumer to improve formulation, processing, and health outcomes. Recent advances in deep learning, natural language processing, and multi-omics integration make it possible to understand and optimize food systems with unprecedented depth. However, AI adoption across the food sector remains uneven due to heterogeneous d","authors":["Xu Zhou","Ivor Prado","AIFPDS participants","Ilias Tagkopoulos"],"url":"http://arxiv.org/abs/2511.15728v1","arxiv_id":"2511.15728v1","published":"2025-11-17T20:17:55Z"},{"title":"Can QE-informed (Re)Translation lead to Error Correction?","abstract":"The paper presents two approaches submitted to the WMT 2025 Automated Translation Quality Evaluation Systems Task 3 - Quality Estimation (QE)-informed Segment-level Error Correction. While jointly training QE systems with Automatic Post-Editing (APE) has shown improved performance for both tasks, APE systems are still known to overcorrect the output of Machine Translation (MT), leading to a degradation in performance","authors":["Govardhan Padmanabhan"],"url":"http://arxiv.org/abs/2511.13884v1","arxiv_id":"2511.13884v1","published":"2025-11-17T20:10:47Z"},{"title":"Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection","abstract":"This paper proposes a new enhanced model architecture to perform classification of lumbar spine degeneration with DICOM images while using a hybrid approach, integrating EfficientNet and VGG19 together with custom-designed components. The proposed model is differentiated from traditional transfer learning methods as it incorporates a Pseudo-Newton Boosting layer along with a Sparsity-Induced Feature Reduction Layer t","authors":["Pandiyaraju V","Abishek Karthik","Jaspin K","Kannan A","Jaime Lloret"],"url":"http://arxiv.org/abs/2511.13877v1","arxiv_id":"2511.13877v1","published":"2025-11-17T19:54:52Z"},{"title":"H-CNN-ViT: A Hierarchical Gated Attention Multi-Branch Model for Bladder Cancer Recurrence Prediction","abstract":"Bladder cancer is one of the most prevalent malignancies worldwide, with a recurrence rate of up to 78%, necessitating accurate post-operative monitoring for effective patient management. Multi-sequence contrast-enhanced MRI is commonly used for recurrence detection; however, interpreting these scans remains challenging, even for experienced radiologists, due to post-surgical alterations such as scarring, swelling, a","authors":["Xueyang Li","Zongren Wang","Yuliang Zhang","Zixuan Pan","Yu-Jen Chen","Nishchal Sapkota"],"url":"http://arxiv.org/abs/2511.13869v2","arxiv_id":"2511.13869v2","published":"2025-11-17T19:39:22Z"},{"title":"Transformer Injectivity & Geometric Robustness - Analytic Margins and Bi-Lipschitz Uniformity of Sequence-Level Hidden States","abstract":"Under real-analytic assumptions on decoder-only Transformers, recent work shows that the map from discrete prompts to last-token hidden states is generically injective on finite prompt sets. We refine this picture: for each layer $\\ell$ we define a collision discriminant $Δ^\\ell \\subset Θ$ and injective stratum $U^\\ell = Θ\\setminus Δ^\\ell$, and prove a dichotomy -- either the model is nowhere injective on the set, or","authors":["Mikael von Strauss"],"url":"http://arxiv.org/abs/2511.14808v1","arxiv_id":"2511.14808v1","published":"2025-11-17T19:39:15Z"},{"title":"Randomized Controlled Trials for Conditional Access Optimization Agent","abstract":"AI agents are increasingly deployed to automate complex enterprise workflows, yet evidence of their effectiveness in identity governance is limited. We report results from the first randomized controlled trial (RCT) evaluating an AI agent for Conditional Access (CA) policy management in Microsoft Entra. The agent assists with four high-value tasks: policy merging, Zero-Trust baseline gap detection, phased rollout pla","authors":["James Bono","Beibei Cheng","Joaquin Lozano"],"url":"http://arxiv.org/abs/2511.13865v1","arxiv_id":"2511.13865v1","published":"2025-11-17T19:33:03Z"},{"title":"Fully Differentiable dMRI Streamline Propagation in PyTorch","abstract":"Diffusion MRI (dMRI) provides a distinctive means to probe the microstructural architecture of living tissue, facilitating applications such as brain connectivity analysis, modeling across multiple conditions, and the estimation of macrostructural features. Tractography, which emerged in the final years of the 20th century and accelerated in the early 21st century, is a technique for visualizing white matter pathways","authors":["Jongyeon Yoon","Elyssa M. McMaster","Michael E. Kim","Gaurav Rudravaram","Kurt G. Schilling","Bennett A. Landman"],"url":"http://arxiv.org/abs/2511.14807v1","arxiv_id":"2511.14807v1","published":"2025-11-17T19:31:47Z"},{"title":"MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging","abstract":"Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit. Relying on either four primitive bases or independently designed DNA tokenizers, existing approaches with naive masked language modeling pre-training often fail to adapt to the varying complexities of genomic sequences. Leveraging Token Me","authors":["Siyuan Li","Kai Yu","Anna Wang","Zicheng Liu","Chang Yu","Jingbo Zhou"],"url":"http://arxiv.org/abs/2511.14806v1","arxiv_id":"2511.14806v1","published":"2025-11-17T19:27:41Z"},{"title":"Randomized Controlled Trials for Phishing Triage Agent","abstract":"Security operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent - the Microsoft Security Copilot Phishing Triage Agent - on analyst productivity and accuracy. Our results demonstrate ","authors":["James Bono"],"url":"http://arxiv.org/abs/2511.13860v1","arxiv_id":"2511.13860v1","published":"2025-11-17T19:23:08Z"},{"title":"Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT","abstract":"SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SNOMED CT is critical for its application but often proves challenging due to linguistic ambiguity, synonymy, polysemy, and so on. This problem is exacerbated when the queries are out-of-vocabulary (OOV), i.e., lacking any equivalent matches in the ontology. In this work, w","authors":["Jonathon Dilworth","Hui Yang","Jiaoyan Chen","Yongsheng Gao","Ernesto Jimenez-Ruiz"],"url":"http://arxiv.org/abs/2511.16698v2","arxiv_id":"2511.16698v2","published":"2025-11-17T19:18:10Z"},{"title":"Reconstruction-Driven Multimodal Representation Learning for Automated Media Understanding","abstract":"Broadcast and media organizations increasingly rely on artificial intelligence to automate the labor-intensive processes of content indexing, tagging, and metadata generation. However, existing AI systems typically operate on a single modality-such as video, audio, or text-limiting their understanding of complex, cross-modal relationships in broadcast material. In this work, we propose a Multimodal Autoencoder (MMAE)","authors":["Yassir Benhammou","Suman Kalyan","Sujay Kumar"],"url":"http://arxiv.org/abs/2511.17596v1","arxiv_id":"2511.17596v1","published":"2025-11-17T19:13:51Z"}],"history":{"events":[{"year":2013,"text":"An outbreak of 77 confirmed tornadoes occurred in seven U.S. states; it became the largest November tornado outbreak in Illinois and Indiana.","url":"https://en.wikipedia.org/wiki/Tornado_outbreak_of_November_17%2C_2013"},{"year":2013,"text":"Tatarstan Airlines Flight 363 crashed during an aborted landing at Kazan International Airport, Russia, killing all 50 people on board and leading to the revocation of the airline's operating certificate.","url":"https://en.wikipedia.org/wiki/Tatarstan_Airlines_Flight_363"},{"year":2009,"text":"Administrators at the University of East Anglia's Climatic Research Unit discovered that their servers had been hacked, and thousands of emails and files on climate change had been stolen.","url":"https://en.wikipedia.org/wiki/University_of_East_Anglia"},{"year":1997,"text":"Sixty-two people were killed by Islamist terrorists outside Deir el-Bahari (temple pictured) in Luxor, one of Egypt's top tourist attractions.","url":"https://en.wikipedia.org/wiki/Luxor_massacre"},{"year":1989,"text":"Walt Disney Pictures released The Little Mermaid to theatres, beginning the Disney Renaissance.","url":"https://en.wikipedia.org/wiki/Walt_Disney_Pictures"},{"year":1968,"text":"NBC controversially cut away from an American football game between the Oakland Raiders and New York Jets to broadcast Heidi, causing viewers in the Eastern United States to miss the game's dramatic ending.","url":"https://en.wikipedia.org/wiki/NBC"},{"year":1950,"text":"The 14th Dalai Lama (pictured) assumed full temporal power as ruler of Tibet at the age of 15.","url":"https://en.wikipedia.org/wiki/14th_Dalai_Lama"},{"year":1943,"text":"World War II: Australian forces launched an assault on Sattelberg, New Guinea, against Japanese forces, initiating the Battle of Sattelberg.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1941,"text":"World War II: Hjalmar Siilasvuo ordered Finnish forces to halt their assault against Soviet forces near Salla, concluding Operation Arctic Fox.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1921,"text":"Rioting broke out in Bombay, India, during the visit of Edward, Prince of Wales, leading to at least 58 deaths.","url":"https://en.wikipedia.org/wiki/Prince_of_Wales_riots"},{"year":1796,"text":"French Revolutionary Wars: French forces won the Battle of Arcole in a manoeuvre to cut the Austrians' line of retreat.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"},{"year":1592,"text":"Sigismund III Vasa, who was already King of Poland, succeeded his father John III as King of Sweden.","url":"https://en.wikipedia.org/wiki/Sigismund_III_Vasa"},{"year":1558,"text":"Elizabeth I became Queen of England and of Ireland, marking the beginning of the Elizabethan era.","url":"https://en.wikipedia.org/wiki/Elizabeth_I"},{"year":1292,"text":"John Balliol was chosen to be King of Scots over Robert de Brus.","url":"https://en.wikipedia.org/wiki/John_Balliol"}],"births":[{"year":2005,"text":"Anna Tanaka, Japanese singer and model","url":"https://en.wikipedia.org/wiki/Anna_Tanaka"},{"year":2004,"text":"Linda Nosková, Czech tennis player","url":"https://en.wikipedia.org/wiki/Linda_Noskov%C3%A1"},{"year":2001,"text":"Kate Douglass, American swimmer","url":"https://en.wikipedia.org/wiki/Kate_Douglass"},{"year":2000,"text":"Joanne Züger, Swiss tennis player","url":"https://en.wikipedia.org/wiki/Joanne_Z%C3%BCger"},{"year":1999,"text":"Gabi Gonçalves, Brazilian politician","url":"https://en.wikipedia.org/wiki/Gabi_Gon%C3%A7alves"},{"year":1997,"text":"Dragan Bender, Croatian basketball player","url":"https://en.wikipedia.org/wiki/Dragan_Bender"},{"year":1997,"text":"Julian Ryerson, Norwegian footballer","url":"https://en.wikipedia.org/wiki/Julian_Ryerson"},{"year":1997,"text":"Yugyeom, South Korean singer","url":"https://en.wikipedia.org/wiki/Yugyeom"}],"deaths":[{"year":2024,"text":"Macoto Takahashi, Japanese manga artist (born 1934)","url":"https://en.wikipedia.org/wiki/Macoto_Takahashi"},{"year":2021,"text":"Young Dolph, American rapper (born 1985)","url":"https://en.wikipedia.org/wiki/Young_Dolph"},{"year":2019,"text":"Tuka Rocha, Brazilian race car driver (born 1982)","url":"https://en.wikipedia.org/wiki/Tuka_Rocha"},{"year":2015,"text":"John Leahy, English lawyer and diplomat, High Commissioner to Australia (born 1928)","url":"https://en.wikipedia.org/wiki/John_Leahy_(diplomat)"},{"year":2015,"text":"Rahim Moeini Kermanshahi, Iranian poet and songwriter (born 1926)","url":"https://en.wikipedia.org/wiki/Rahim_Moeini_Kermanshahi"},{"year":2014,"text":"John T. Downey, American CIA agent and judge (born 1930)","url":"https://en.wikipedia.org/wiki/John_T._Downey"},{"year":2014,"text":"Bill Frenzel, American lieutenant and politician (born 1928)","url":"https://en.wikipedia.org/wiki/Bill_Frenzel"},{"year":2014,"text":"Ray Sadecki, American baseball player (born 1940)","url":"https://en.wikipedia.org/wiki/Ray_Sadecki"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}