{"date":"2026-06-29","schema_version":1,"captured_at":"2026-08-25T23:14:05.145Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":14},"attention":[{"title":"2026 FIFA World Cup","views":1405447,"rank":2,"extract":"The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. 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terminal","points":166,"comments":110,"url":"https://github.com/ogulcancelik/herdr","discussion":"https://news.ycombinator.com/item?id=48714802"}],"research":[{"title":"Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space","abstract":"Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially deep residual networks (ResNets), tend to learn during training remain insufficiently characterized. To this end, we model the forward propagation of deep residual networks using continuity equations, in which the measure is conserved and infinite curves in ","authors":["Gai, Kuo","Zhang, Shihua"],"url":"https://arxiv.org/abs/2102.09235","arxiv_id":"2102.09235","published":"2026-06-29"},{"title":"Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions","abstract":"There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on the performance metrics used to evaluate and compare them -- resulting in a growing body of literature to evaluate and compare metrics. This paper strives for a more balanced perspective on classifier performance metrics by","authors":["Lovell, David","Miller, Dimity","Capra, Jaiden","Bradley, Andrew"],"url":"https://arxiv.org/abs/2206.02157","arxiv_id":"2206.02157","published":"2026-06-29"},{"title":"Multiperiodic Processes: Ergodic Sources with a Sublinear Entropy","abstract":"Several explicit stochastic processes are known to satisfy Hilberg's law, a power-law growth of block entropy conjectured for natural language and recently connected to the neural scaling law. Existing examples either possess a positive Shannon entropy rate, are non-ergodic, or require comparatively involved constructions. We introduce multiperiodic processes, a new class of stationary ergodic processes over the ","authors":["Dębowski, Łukasz"],"url":"https://arxiv.org/abs/2302.09049","arxiv_id":"2302.09049","published":"2026-06-29"},{"title":""Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets","abstract":"Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive data","authors":["Liu, Jin","Xu, Xingchen","Nan, Xi","Li, Yongjun","Tan, Yong"],"url":"https://arxiv.org/abs/2308.05201","arxiv_id":"2308.05201","published":"2026-06-29"},{"title":"Arrows of Time for Large Language Models","abstract":"We study the probabilistic modeling performed by Autoregressive Large Language Models (LLMs) through the angle of time directionality, addressing a question first raised in (Shannon, 1951). For large enough models, we empirically find a time asymmetry in their ability to learn natural language: a difference in the average log-perplexity when trying to predict the next token versus when trying to predict the previous ","authors":["Papadopoulos, Vassilis","Wenger, Jérémie","Hongler, Clément"],"url":"https://arxiv.org/abs/2401.17505","arxiv_id":"2401.17505","published":"2026-06-29"},{"title":"Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding","abstract":"Kernel herding belongs to a family of deterministic quadratures that seek to minimize the maximum mean discrepancy (MMD), that is, the worst-case integration error over a reproducing kernel Hilbert space (RKHS). These MMD minimization procedures come with strong experimental support, but comparatively less theoretical footing. In particular, apart from recent progress in distribution compression, little has been prov","authors":["Rouault, Martin","Bardenet, Rémi","Maïda, Mylène"],"url":"https://arxiv.org/abs/2402.11736","arxiv_id":"2402.11736","published":"2026-06-29"},{"title":"ROME: Memorization Insights from Text, Logits and Representation","abstract":"Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is highly time-consuming. To address this challenge, this paper proposes an innovative approach named ROME that bypasses direct processing of the training data. Specifically","authors":["Li, Bo","Zhao, Qinghua","Wen, Lijie"],"url":"https://arxiv.org/abs/2403.00510","arxiv_id":"2403.00510","published":"2026-06-29"},{"title":"MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data","abstract":"In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can address this, but existing models typically require substantial amounts of data to train effectively, contradicting our objective to solve data scarcity. To address this challe","authors":["Ling, Yaobin","Jiang, Xiaoqian","Kim, Yejin"],"url":"https://arxiv.org/abs/2406.10521","arxiv_id":"2406.10521","published":"2026-06-29"},{"title":"An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations","abstract":"Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is scarce. In this paper, we propose EMDiffusion, an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean image","authors":["Bai, Weimin","Wang, Yifei","Chen, Wenzheng","Sun, He"],"url":"https://arxiv.org/abs/2407.01014","arxiv_id":"2407.01014","published":"2026-06-29"},{"title":"Graph Unfolding and Sampling for Transitory Video Keyframe Selection via Gershgorin Disc Alignment","abstract":"User-generated videos (UGVs) uploaded from mobile phones to social media sites like YouTube and TikTok are short and non-repetitive. We summarize a transitory UGV into several keyframes in linear-time via fast graph sampling based on Gershgorin disc alignment (GDA). Specifically, we first model a sequence of $N$ frames in a UGV as an $M$-hop path graph $\\cG^o$ for $M \\ll N$, where the similarity between two frames wi","authors":["Sahami, Sadid","Cheung, Gene","Lin, Chia-Wen"],"url":"https://arxiv.org/abs/2408.01859","arxiv_id":"2408.01859","published":"2026-06-29"},{"title":"ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering","abstract":"Domain-specific question answering remains challenging for language models, given the deep technical knowledge required to answer questions correctly. This difficulty is amplified for smaller language models that cannot encode as much information in their parameters as larger models. The "Specializing Large Language Models for Telecom Networks" challenge aimed to enhance the performance of two small language ","authors":["Gichamba, Alex","Idris, Tewodros Kederalah","Ebiyau, Brian","Nyberg, Eric","Mitamura, Teruko"],"url":"https://arxiv.org/abs/2408.10808","arxiv_id":"2408.10808","published":"2026-06-29"},{"title":"iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework","abstract":"Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class. Cost-sensitive learning (CSL) addresses this issue by assigning higher penalties to minority-class misclassifications. However, conventional CSL typically applies a uniform penalty to all minority-class instances, ignoring the fact that minority samples may diff","authors":["Newaz, Asif","Adib, Asif Ur Rahman","Jabid, Taskeed"],"url":"https://arxiv.org/abs/2409.13007","arxiv_id":"2409.13007","published":"2026-06-29"},{"title":"MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis","abstract":"Recent works in volume rendering, \\textit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with the help of the learned implicit neural radiance field or 3D Gaussians. Rendering on top of an explicit representation, the vanilla 3DGS and its variants deliver real-time efficiency by optimizing the parametric model with single-view supervision per iteration during t","authors":["Du, Xiaobiao","Wang, Yida","Yu, Xin"],"url":"https://arxiv.org/abs/2410.02103","arxiv_id":"2410.02103","published":"2026-06-29"},{"title":"Continual Memorization of Factoids in Language Models","abstract":"As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is fine-tuning them directly on new knowledge. However, recent studies have shown that fine-tuning for memorization may be ineffective in storing knowledge or may exacerbate hallucinations. In this work, we introduce a setting we call continual memorization, where a model mu","authors":["Chen, Howard","Geng, Jiayi","Bhaskar, Adithya","Friedman, Dan","Chen, Danqi"],"url":"https://arxiv.org/abs/2411.07175","arxiv_id":"2411.07175","published":"2026-06-29"},{"title":"Tortho-Gaussian: Splatting True Digital Orthophoto Maps","abstract":"True Digital Orthophoto Maps (TDOMs) are essential products for digital twins and Geographic Information Systems (GIS). Traditionally, TDOM generation involves a complex set of traditional photogrammetric process, which may deteriorate due to various challenges, including inaccurate Digital Surface Model (DSM), degenerated occlusion detections, and visual artifacts in weak texture regions and reflective surfaces, etc","authors":["Wang, Xin","Zhang, Wendi","Xie, Hong","Ai, Haibin","Yuan, Qiangqiang","Zhan, Zongqian"],"url":"https://arxiv.org/abs/2411.19594","arxiv_id":"2411.19594","published":"2026-06-29"},{"title":"SpecFuse: Ensembling Large Language Models via Next-Segment Prediction","abstract":"Ensembles of generative large language models (LLMs) are a promising way to compensate for individual model limitations, integrating the strengths of different LLMs. Existing LLM ensemble methods, however, face limitations such as first-token delay and challenges in long-range semantic collaboration between models, Moreover, they typically assume equal voting weights for all models during ensemble, ignoring task-spec","authors":["Lv, Bo","Liu, Nayu","Tang, Chen","Liu, Xin","Yu, Yue","Luo, Ping"],"url":"https://arxiv.org/abs/2412.07380","arxiv_id":"2412.07380","published":"2026-06-29"},{"title":"Towards Reliable Recommender Systems for Rating Data","abstract":"Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place increasing pressure on the reliability of these systems. A common statistical approach behind recommender systems is so-called matrix completion, which predicts how users would rate items the","authors":["Archimbaud, Aurore","Alfons, Andreas","Wilms, Ines"],"url":"https://arxiv.org/abs/2412.20802","arxiv_id":"2412.20802","published":"2026-06-29"},{"title":"Automating RT Planning at Scale: High Quality Data For AI Training","abstract":"Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is desig","authors":["Gao, Riqiang","Diallo, Mamadou","Liu, Han","Magliari, Anthony","Sackett, Jonathan","Verbakel, Wilko"],"url":"https://arxiv.org/abs/2501.11803","arxiv_id":"2501.11803","published":"2026-06-29"},{"title":"Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?","abstract":"The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization - the problem of identifying the geo-coordinates of a place based on visual data only. In robotics, such capabilities are particularly relevant to the global re-localization stage of the kidnapped robot problem, where a robot must recover its pose without prior knowledge of its location","authors":["Waheed, Sania","Ferrarini, Bruno","Milford, Michael","Ramchurn, Sarvapali D.","Ehsan, Shoaib"],"url":"https://arxiv.org/abs/2501.16947","arxiv_id":"2501.16947","published":"2026-06-29"},{"title":"Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction","abstract":"Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation. A common strategy is to learn features that maximize a measure of statistical dissimilarity between the class-conditional probability distributions. Information geometry, which is rooted in Riemannian geometry, provides an alternative framework for measuring class dissimilarity. It treats prob","authors":["Herrera-Esposito, Daniel","Burge, Johannes"],"url":"https://arxiv.org/abs/2502.00168","arxiv_id":"2502.00168","published":"2026-06-29"},{"title":"LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning","abstract":"Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-context performance of arbitrary short-context LLMs by dynamically adapting their parameters to each long input. Instead of endlessly extending context windows to fit longer inputs in context, LIFT stores and absorbs the input in parameters. By","authors":["Mao, Yansheng","Xu, Yufei","Li, Jiaqi","Meng, Fanxu","Yang, Haotong","Zheng, Zilong"],"url":"https://arxiv.org/abs/2502.14644","arxiv_id":"2502.14644","published":"2026-06-29"},{"title":"Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians","abstract":"Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts. The Gumbel-Sinkhorn method, while effective, requires N*N parameters to determine a full permutation matrix, making it computationally expensive for large datasets. Low-rank matrix factorization approximations reduce memory requirements to 2NM (with ","authors":["Barthel, Kai Uwe","Barthel, Florian","Eisert, Peter"],"url":"https://arxiv.org/abs/2503.13051","arxiv_id":"2503.13051","published":"2026-06-29"},{"title":"Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method","abstract":"Trajectory prediction, as a critical component of autonomous driving systems, has attracted the attention of many researchers. Existing prediction algorithms focus on extracting more detailed scene features or selecting more reasonable trajectory destinations. However, in the face of dynamic and evolving future movements of the target vehicle, these algorithms cannot provide a fine-grained and continuous description ","authors":["Xiong, Wenyi","Chen, Jian","Qi, Ziheng"],"url":"https://arxiv.org/abs/2503.21477","arxiv_id":"2503.21477","published":"2026-06-29"},{"title":"CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection","abstract":"The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious actors to bypass traditional monitoring and intrusion detection systems while evading detection by conventional traffic analysis techniques. ML techniques can be used to detect DoH tunnels; however, their effectiveness relies on large datas","authors":["Cajaraville-Aboy, Diego","Moure-Garrido, Marta","Beis-Penedo, Carlos","Garcia-Rubio, Carlos","Díaz-Redondo, Rebeca P.","Campo, Celeste"],"url":"https://arxiv.org/abs/2504.01882","arxiv_id":"2504.01882","published":"2026-06-29"},{"title":"Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance","abstract":"We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multi-dimensional setting, and make new arguments about the proper way to approach this generalization. Our proposed formulation maximizes the difference over orthogonal dominating rectangular ranges (d-sided rectangles in R^d), and is an integral probability metric. We also prove that the distance between a distribution and","authors":["Jacobs, Peter Matthew","Namjoo, Foad","Phillips, Jeff M."],"url":"https://arxiv.org/abs/2504.11299","arxiv_id":"2504.11299","published":"2026-06-29"}],"history":{"events":[{"year":2020,"text":"Reddit banned r/The_Donald, a pro-Trump subreddit, for rule violations and antagonizing the company.","url":"https://en.wikipedia.org/wiki/Reddit"},{"year":2003,"text":"An overloaded balcony collapsed in Chicago, United States, killing 13 people and injuring 57 others.","url":"https://en.wikipedia.org/wiki/2003_Chicago_balcony_collapse"},{"year":1995,"text":"Atlantis became the first U.S. Space Shuttle to dock with the Russian space station Mir as part of the Shuttle–Mir program.","url":"https://en.wikipedia.org/wiki/Space_Shuttle_Atlantis"},{"year":1971,"text":"The Soyuz 11 spacecraft experienced uncontrolled decompression during preparations for reentry, killing Soviet cosmonauts Vladislav Volkov, Georgy Dobrovolsky and Viktor Patsayev—the only human deaths to have occurred in space.","url":"https://en.wikipedia.org/wiki/Soyuz_11"},{"year":1967,"text":"Actress Jayne Mansfield (pictured), her boyfriend Sam Brody, and their driver were killed in a car accident outside of New Orleans, while her children Miklós, Zoltán, and Mariska Hargitay escaped with only minor injuries.","url":"https://en.wikipedia.org/wiki/Jayne_Mansfield"},{"year":1950,"text":"The United States defeated England during the FIFA World Cup in one of the greatest upsets in the competition's history.","url":"https://en.wikipedia.org/wiki/United_States_men's_national_soccer_team"},{"year":1927,"text":"The United States Army Air Corps aircraft Bird of Paradise landed at Wheeler Field on the Hawaiian island of Oahu to complete the first transpacific flight.","url":"https://en.wikipedia.org/wiki/United_States_Army_Air_Corps"},{"year":1913,"text":"More than 50,000 Union and Confederate veterans gathered at the Gettysburg Battlefield, the largest combined reunion of American Civil War veterans ever held.","url":"https://en.wikipedia.org/wiki/Union_army"},{"year":1889,"text":"Hyde Park and several other Illinois townships voted to be annexed by Chicago, forming the largest city by area in the United States and the second-largest by population.","url":"https://en.wikipedia.org/wiki/Hyde_Park_Township%2C_Illinois"},{"year":1864,"text":"A passenger train fell through an open swing bridge into the Richelieu River near present-day Mont-Saint-Hilaire, Quebec, killing as many as 99 people and injuring 100 others in Canada's worst railway accident.","url":"https://en.wikipedia.org/wiki/Swing_bridge"},{"year":1832,"text":"Black Hawk War: Three men working in a cornfield were attacked by Sauk Native Americans, resulting in two deaths.","url":"https://en.wikipedia.org/wiki/Black_Hawk_War"},{"year":1776,"text":"The first privateer battle of the American Revolutionary War, the Battle of Turtle Gut Inlet, was fought near Cape May, New Jersey (map of location, pictured).","url":"https://en.wikipedia.org/wiki/Privateer"},{"year":1764,"text":"One of the strongest tornadoes in history struck Woldegk in present-day northeastern Germany, killing one person.","url":"https://en.wikipedia.org/wiki/1764_Woldegk_tornado"},{"year":1613,"text":"The original Globe Theatre in London burned to the ground after a cannon employed for special effects misfired during a performance of Henry VIII and ignited the roof.","url":"https://en.wikipedia.org/wiki/Globe_Theatre"}],"births":[{"year":2007,"text":"Casey Phair, South Korean footballer","url":"https://en.wikipedia.org/wiki/Casey_Phair"},{"year":2006,"text":"Sam Lavagnino, American child voice actor","url":"https://en.wikipedia.org/wiki/Sam_Lavagnino"},{"year":2003,"text":"Jude Bellingham, English footballer","url":"https://en.wikipedia.org/wiki/Jude_Bellingham"},{"year":2002,"text":"Matt Rempe, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Matt_Rempe"},{"year":2001,"text":"Julian Champagnie, American basketball player","url":"https://en.wikipedia.org/wiki/Julian_Champagnie"},{"year":2001,"text":"Gunnar Henderson, American baseball player","url":"https://en.wikipedia.org/wiki/Gunnar_Henderson"},{"year":2001,"text":"Aaron Schoupp, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Aaron_Schoupp"},{"year":1998,"text":"Michael Porter Jr., American basketball player","url":"https://en.wikipedia.org/wiki/Michael_Porter_Jr."}],"deaths":[{"year":2025,"text":"Sandy Gall, Malaysian-Scottish journalist and author (born 1927)","url":"https://en.wikipedia.org/wiki/Sandy_Gall"},{"year":2024,"text":"Princess Lalla Latifa, Princess Dowager of Morocco (born 1946)","url":"https://en.wikipedia.org/wiki/Lalla_Latifa_Amahzoune"},{"year":2023,"text":"Alan Arkin, American actor (born 1934)","url":"https://en.wikipedia.org/wiki/Alan_Arkin"},{"year":2022,"text":"Hershel W. Williams, American Marine Corps warrant officer, last living Medal of Honor recipient from World War II (born 1923)","url":"https://en.wikipedia.org/wiki/Hershel_W._Williams"},{"year":2021,"text":"Donald Rumsfeld, American captain and politician, 13th United States Secretary of Defense (born 1932)","url":"https://en.wikipedia.org/wiki/Donald_Rumsfeld"},{"year":2020,"text":"Carl Reiner, American actor, director, producer, and screenwriter (born 1922)","url":"https://en.wikipedia.org/wiki/Carl_Reiner"},{"year":2020,"text":"Stepa J. Groggs, American rap artist (born 1988)","url":"https://en.wikipedia.org/wiki/Injury_Reserve"},{"year":2020,"text":"Hachalu Hundessa, Ethiopian singer, songwriter (born 1986)","url":"https://en.wikipedia.org/wiki/Hachalu_Hundessa"}]},"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."}