{"date":"2026-06-12","schema_version":1,"captured_at":"2026-08-25T23:12:06.178Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"2026 FIFA World Cup","views":753751,"rank":3,"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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sounds","points":180,"comments":91,"url":"https://doug.sh/posts/erm-a-local-cli-that-strips-ums-uhs-and-erms-from-speech/","discussion":"https://news.ycombinator.com/item?id=48498421"},{"title":"Adaptive PDFs","points":170,"comments":81,"url":"https://sgaud.com/texts/pdf","discussion":"https://news.ycombinator.com/item?id=48506209"}],"research":[{"title":"full-FORCE: A Target-Based Method for Training Recurrent Networks","abstract":"Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it e","authors":["DePasquale, Brian","Cueva, Christopher J.","Rajan, Kanaka","Escola, G. Sean","Abbott, L. F."],"url":"https://arxiv.org/abs/1710.03070","arxiv_id":"1710.03070","published":"2026-06-12"},{"title":"Synchronization of Tree Parity Machines using non-binary input vectors","abstract":"Neural cryptography is the application of artificial neural networks in the subject of cryptography. The functionality of this solution is based on a tree parity machine. It uses artificial neural networks to perform secure key exchange between network entities. This article proposes improvements to the synchronization of two tree parity machines. The improvement is based on learning artificial neural network using i","authors":["Stypiński, Miłosz","Niemiec, Marcin"],"url":"https://arxiv.org/abs/2104.11105","arxiv_id":"2104.11105","published":"2026-06-12"},{"title":"On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators","abstract":"This paper develops an Operator Learning framework for approximating the dynamic response of synchronous generators. The framework can be used to (i) build a neural network-based generator model that interacts with a power grid simulator or (ii) shadow the true generator's transient response. First, we develop a data-driven Deep Operator Network (DeepONet) to approximate the infinite-dimensional solution operator","authors":["Moya, Christian","Mollaali, Amirhossein","Lin, Guang","Yue, Meng"],"url":"https://arxiv.org/abs/2301.12538","arxiv_id":"2301.12538","published":"2026-06-12"},{"title":"On Pitfalls of $\\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective","abstract":"The RemOve-And-Retrain (ROAR) benchmark is widely used to evaluate feature attribution methods, yet its validity remains underexplored from an information-theoretic perspective. We show that model- and data-agnostic post-processing of attribution maps (transformations that, by the data processing inequality, \\emph{cannot} add information about the decision function) can often improve ROAR scores. This means that an i","authors":["Song, Junhwa","Cha, Keumgang","Seo, Junghoon"],"url":"https://arxiv.org/abs/2304.13836","arxiv_id":"2304.13836","published":"2026-06-12"},{"title":"ResidualPlanner+: a scalable matrix mechanism for marginals and beyond","abstract":"Noisy marginals are a common form of confidentiality protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation. Privacy mechanisms that provide unbiased noisy answers to linear queries (such as marginals) are known as matrix mechanisms. We propose ResidualPlanner and ResidualPlanner+, two highly scalable m","authors":["He, Guanlin","Xiao, Yingtai","Toksoz, Levent","Ding, Zeyu","Zhang, Danfeng","Kifer, Daniel"],"url":"https://arxiv.org/abs/2305.08175","arxiv_id":"2305.08175","published":"2026-06-12"},{"title":"Context selectivity with dynamic availability enables lifelong continual learning","abstract":""You never forget how to ride a bike", -- but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capability, referred to as lifelong learning (or continual learning, CL), are unknown. We suggest a bio-plausible meta-plasticity rule building on classical wor","authors":["Barry, Martin","Gerstner, Wulfram","Bellec, Guillaume"],"url":"https://arxiv.org/abs/2306.01690","arxiv_id":"2306.01690","published":"2026-06-12"},{"title":"Estimating Deep Learning energy consumption based on model architecture and training environment","abstract":"To raise awareness of the environmental impact of deep learning (DL), many studies estimate the energy use of DL systems. However, energy estimates during DL training often rely on unverified assumptions. This work addresses that gap by investigating how model architecture and training environment affect energy consumption. We train a variety of computer vision models and collect energy consumption and accuracy metri","authors":["del Rey, Santiago","Cruz, Luís","Franch, Xavier","Martínez-Fernández, Silverio"],"url":"https://arxiv.org/abs/2307.05520","arxiv_id":"2307.05520","published":"2026-06-12"},{"title":"QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS","abstract":"In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (S","authors":["Yeganeh, Rahman Saadat","Omidi, Mohammad Javad","Zeinali, Farshad","Mili, Mohammad Robat","Ghavami, Mohammad"],"url":"https://arxiv.org/abs/2401.08301","arxiv_id":"2401.08301","published":"2026-06-12"},{"title":"Plug-and-Play image restoration with Stochastic deNOising REgularization","abstract":"Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Model","authors":["Renaud, Marien","Prost, Jean","Leclaire, Arthur","Papadakis, Nicolas"],"url":"https://arxiv.org/abs/2402.01779","arxiv_id":"2402.01779","published":"2026-06-12"},{"title":"Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction","abstract":"Document collections of various domains, e.g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both human users and structure-aware models. We propose to identify the typical structure of document within a collection, which requires to capture recurring topics across the collection, while abstracting over arbitrary header paraphrases, and gr","authors":["Lior, Gili","Goldberg, Yoav","Stanovsky, Gabriel"],"url":"https://arxiv.org/abs/2402.13906","arxiv_id":"2402.13906","published":"2026-06-12"},{"title":"Geometry of Lightning Self-Attention: Identifiability and Dimension","abstract":"We consider function spaces defined by self-attention networks without normalization, and theoretically analyze their geometry. Since these networks are polynomial, we rely on tools from algebraic geometry. In particular, we study the identifiability of deep attention by providing a description of the generic fibers of the parametrization for an arbitrary number of layers and, as a consequence, compute the dimension ","authors":["Henry, Nathan W.","Marchetti, Giovanni Luca","Kohn, Kathlén"],"url":"https://arxiv.org/abs/2408.17221","arxiv_id":"2408.17221","published":"2026-06-12"},{"title":"Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments","abstract":"In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We sho","authors":["Imai, Kosuke","Nakamura, Kentaro"],"url":"https://arxiv.org/abs/2410.00903","arxiv_id":"2410.00903","published":"2026-06-12"},{"title":"P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction","abstract":"Ever since the Dennard scaling broke down in the early 2000s and the frequency of the CPUs stalled, vendors have started to increase the core count in each CPU chip at the expense of introducing heterogeneity, thus ushering the era of NUMA and Chiplet processors. Since then, the heterogeneity in the design space of hardware has only increased to the point that DBMS performance may vary significantly up to an order of","authors":["Rayhan, Yeasir","Aref, Walid G."],"url":"https://arxiv.org/abs/2411.02933","arxiv_id":"2411.02933","published":"2026-06-12"},{"title":"Competition and Diversity in Generative AI","abstract":"Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or similar AI models appears to lead to more homogeneous behavior. Our work begins with the observation that there is a force pushing in the opposite direction: competition. When producers compete with one another (e.g., for customers or attention","authors":["Raghavan, Manish"],"url":"https://arxiv.org/abs/2412.08610","arxiv_id":"2412.08610","published":"2026-06-12"},{"title":"Review of Fruit Tree Image Segmentation","abstract":"Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 158 relevant papers collected using a newly designed crawling ","authors":["Oh, Il-Seok"],"url":"https://arxiv.org/abs/2412.14631","arxiv_id":"2412.14631","published":"2026-06-12"},{"title":"Is Stochastic Gradient Descent Effective? A PDE Perspective on Machine Learning processes","abstract":"In this paper we analyze the behaviour of the stochastic gradient descent (SGD), a widely used method in supervised learning for optimizing neural network weights via a minimization of non-convex loss functions. Since the pioneering work of E, Li and Tai (2017), the underlying structure of such processes can be understood via parabolic PDEs of Fokker-Planck type, which are at the core of our analysis. Even if Fokker-","authors":["Barbieri, Davide","Bonforte, Matteo","Ibarrondo, Peio"],"url":"https://arxiv.org/abs/2501.08425","arxiv_id":"2501.08425","published":"2026-06-12"},{"title":"Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators","abstract":"This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor strongly convex. Beyond conventional perspectives and techniques, we view quantile SGD iteration as an irreducible, periodic, and positive recurrent Markov chain, which cyclically converges to its unique stationary distribution regardless o","authors":["Wei, Ziyang","Li, Jiaqi","Chen, Likai","Wu, Wei Biao"],"url":"https://arxiv.org/abs/2503.02178","arxiv_id":"2503.02178","published":"2026-06-12"},{"title":"WildIFEval: Instruction Following in the Wild","abstract":"Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge. In this work, we introduce WildIFEval - a large-scale dataset of 7K real user instructions with diverse, multi-constraint conditions. Unlike prior datasets, our collection spans a broad lexical and topical spectrum of constraints, extracted from natural user in","authors":["Lior, Gili","Yehudai, Asaf","Gera, Ariel","Ein-Dor, Liat"],"url":"https://arxiv.org/abs/2503.06573","arxiv_id":"2503.06573","published":"2026-06-12"},{"title":"Oncomorphic neural agent populations for resource-limited sequential learning","abstract":"Distributed artificial intelligence (AI) often operates under sequential task exposure, uneven compute, and decentralized coordination. Here, we present a cancer-inspired, or oncomorphic, multi-agent framework in which simulated neural agents can replicate, mutate their neural network architecture, migrate across task environments, undergo ecological turnover, and recruit learning/ecological resources from a finite s","authors":["Greulich, Philip","Levin, Michael","Moreddu, Rosalia"],"url":"https://arxiv.org/abs/2503.12743","arxiv_id":"2503.12743","published":"2026-06-12"},{"title":"Radar-Guided Polynomial Fitting for Metric Depth Estimation","abstract":"We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps. Unlike existing approaches that rely on complex architectures or expensive sensors, our method is grounded in a fundamental insight: although MDE models often infer reasonable local depth s","authors":["Rim, Patrick","Park, Hyoungseob","Ezhov, Vadim","Moon, Jeffrey","Wong, Alex"],"url":"https://arxiv.org/abs/2503.17182","arxiv_id":"2503.17182","published":"2026-06-12"},{"title":"Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning","abstract":"Multimodal agents, which integrate a controller e.g., a vision language model) with external tools, have demonstrated remarkable capabilities in tackling complex multimodal tasks. Existing approaches for training these agents, both supervised fine-tuning and reinforcement learning, depend on extensive human-annotated task-answer pairs and tool trajectories. However, for complex multimodal tasks, such annotations are ","authors":["Li, Pengxiang","Gao, Zhi","Zhang, Bofei","Mi, Yapeng","Ma, Xiaojian","Shi, Chenrui"],"url":"https://arxiv.org/abs/2504.21561","arxiv_id":"2504.21561","published":"2026-06-12"},{"title":"Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning","abstract":"Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall. Analysis of production traces reveals a dynamic bursty-group pattern in which sets of models become active together and shift over time; existing space- and time-sharing approaches lack principled mechanisms to adapt to this variability, forci","authors":["Yu, Shan","Qiao, Yifan","Ma, Mingyuan","Li, Yangmin","Yang, Shuo","Tong, Xinyuan"],"url":"https://arxiv.org/abs/2505.04021","arxiv_id":"2505.04021","published":"2026-06-12"},{"title":"Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks","abstract":"We study function spaces parametrized by neural networks, referred to as neuromanifolds. Specifically, we focus on deep Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) with an activation function that is a sufficiently generic polynomial. First, we address the identifiability problem, showing that, for almost all functions in the neuromanifold of an MLP, there exist only finitely many paramete","authors":["Shahverdi, Vahid","Marchetti, Giovanni Luca","Kohn, Kathlén"],"url":"https://arxiv.org/abs/2505.11846","arxiv_id":"2505.11846","published":"2026-06-12"},{"title":"Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast","abstract":"Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\\mathcal{G}","authors":["Qi, Ji","Do, Tam Thuc","Liu, Mingxiao","Pan, Zhuoshi","Li, Yuzhe","Cheung, Gene"],"url":"https://arxiv.org/abs/2505.13102","arxiv_id":"2505.13102","published":"2026-06-12"},{"title":"ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior","abstract":"Post-hoc interpretability methods typically attribute a model's behavior to its components, data, or training trajectory in isolation, and are often tied to a particular level of granularity along the local-to-global spectrum. This leads to explanations that lack a unified view and may miss key interactions. We present ExPLAIND, a theoretically grounded, unified framework that integrates model components, data, a","authors":["Eichin, Florian","Du, Yupei","Mondorf, Philipp","Matveev, Maria","Plank, Barbara","Hedderich, Michael A."],"url":"https://arxiv.org/abs/2505.20076","arxiv_id":"2505.20076","published":"2026-06-12"}],"history":{"events":[{"year":2025,"text":"Air India Flight 171 (wreckage pictured) crashed shortly after take-off in Ahmedabad, India, killing 260 people.","url":"https://en.wikipedia.org/wiki/Air_India_Flight_171"},{"year":2016,"text":"An Islamic terrorist killed 49 people in a mass shooting at the gay nightclub Pulse in Orlando, Florida.","url":"https://en.wikipedia.org/wiki/Pulse_nightclub_shooting"},{"year":2010,"text":"Sixteen-year-old Abby Sunderland was rescued after her boat was dismasted in the Indian Ocean while trying to become the youngest sailor around the world.","url":"https://en.wikipedia.org/wiki/Abby_Sunderland"},{"year":1999,"text":"In the aftermath of the bombing of Yugoslavia and the Kosovo War, the NATO-led Kosovo Force entered the territory with a mandate of establishing a secure environment.","url":"https://en.wikipedia.org/wiki/NATO_bombing_of_Yugoslavia"},{"year":1994,"text":"The Boeing 777, the world's largest twinjet, made its maiden flight.","url":"https://en.wikipedia.org/wiki/Boeing_777"},{"year":1991,"text":"More than 150 Sri Lankan Tamil civilians were massacred by members of the military in the village of Kokkadichcholai.","url":"https://en.wikipedia.org/wiki/Sri_Lankan_Tamils"},{"year":1987,"text":"Cold War: During a speech at the Brandenburg Gate by the Berlin Wall, US president Ronald Reagan challenged Soviet leader Mikhail Gorbachev to \"tear down this wall!\" (speech featured).","url":"https://en.wikipedia.org/wiki/Cold_War"},{"year":1981,"text":"Raiders of the Lost Ark, the first film in the Indiana Jones franchise, was released.","url":"https://en.wikipedia.org/wiki/Raiders_of_the_Lost_Ark"},{"year":1978,"text":"American serial killer David Berkowitz, popularly known as the \"Son of Sam\", was sentenced to 25-years-to-life in prison for each of six killings.","url":"https://en.wikipedia.org/wiki/David_Berkowitz"},{"year":1967,"text":"The U.S. Supreme Court struck down laws restricting interracial marriage in the landmark civil rights case Loving v. Virginia.","url":"https://en.wikipedia.org/wiki/Supreme_Court_of_the_United_States"},{"year":1963,"text":"The premiere was held in New York of the historical drama film Cleopatra, the most expensive film made to that point.","url":"https://en.wikipedia.org/wiki/Cleopatra_(1963_film)"},{"year":1954,"text":"Dominic Savio, who was 14 years old at his death in 1857, was canonized by Pope Pius XII, making him one of the youngest non-martyred saints in the Catholic Church.","url":"https://en.wikipedia.org/wiki/Dominic_Savio"},{"year":1942,"text":"On her thirteenth birthday, Anne Frank (pictured) began keeping a diary during the Nazi occupation of the Netherlands in World War II.","url":"https://en.wikipedia.org/wiki/Anne_Frank"},{"year":1921,"text":"Soviet politician Vladimir Antonov-Ovseenko authorised the use of chemical weapons against a peasant rebellion in the Tambov Governorate.","url":"https://en.wikipedia.org/wiki/Vladimir_Antonov-Ovseenko"},{"year":1914,"text":"As part of the Ottoman Empire's policies of ethnic cleansing, Turkish irregulars began a six-day massacre in the predominantly Greek town of Phocaea.","url":"https://en.wikipedia.org/wiki/Ottoman_Empire"},{"year":1864,"text":"Union general Ulysses S. Grant pulled his troops out of the Battle of Cold Harbor in Hanover County, Virginia, ending one of the bloodiest, most lopsided battles in the American Civil War.","url":"https://en.wikipedia.org/wiki/Union_(American_Civil_War)"},{"year":1798,"text":"Following the successful French invasion of Malta, the Knights Hospitaller surrendered Malta to Napoleon, initiating two years of occupation.","url":"https://en.wikipedia.org/wiki/French_invasion_of_Malta"},{"year":1776,"text":"The Fifth Virginia Convention adopted a declaration of rights, an influential document that proclaimed the inherent rights of men.","url":"https://en.wikipedia.org/wiki/Fifth_Virginia_Convention"}],"births":[{"year":2002,"text":"Koni De Winter, Belgian footballer","url":"https://en.wikipedia.org/wiki/Koni_De_Winter"},{"year":1999,"text":"CarryMinati, Indian YouTuber","url":"https://en.wikipedia.org/wiki/CarryMinati"},{"year":1996,"text":"Gustav Forsling, Swedish ice hockey player","url":"https://en.wikipedia.org/wiki/Gustav_Forsling"},{"year":1996,"text":"Davinson Sánchez, Colombian footballer","url":"https://en.wikipedia.org/wiki/Davinson_S%C3%A1nchez"},{"year":1996,"text":"Shonica Wharton, Barbadian netball player","url":"https://en.wikipedia.org/wiki/Shonica_Wharton"},{"year":1994,"text":"Don Toliver, American rapper and singer-songwriter","url":"https://en.wikipedia.org/wiki/Don_Toliver"},{"year":1992,"text":"Philippe Coutinho, Brazilian footballer","url":"https://en.wikipedia.org/wiki/Philippe_Coutinho"},{"year":1992,"text":"Allie DiMeco, American actress and musician","url":"https://en.wikipedia.org/wiki/Allie_DiMeco"}],"deaths":[{"year":2024,"text":"William H. Donaldson, American businessman (born 1931)","url":"https://en.wikipedia.org/wiki/William_H._Donaldson"},{"year":2024,"text":"Neil Goldschmidt, American lawyer and politician, 33rd Governor of Oregon (born 1940)","url":"https://en.wikipedia.org/wiki/Neil_Goldschmidt"},{"year":2024,"text":"Jerry West, American basketball player and executive (born 1938)","url":"https://en.wikipedia.org/wiki/Jerry_West"},{"year":2023,"text":"Silvio Berlusconi, Italian businessman and politician, Prime Minister of Italy (born 1936)","url":"https://en.wikipedia.org/wiki/Silvio_Berlusconi"},{"year":2023,"text":"Francesco Nuti, Italian actor and director, (born 1955)","url":"https://en.wikipedia.org/wiki/Francesco_Nuti"},{"year":2023,"text":"John Romita Sr., comic book artist and author (born 1930)","url":"https://en.wikipedia.org/wiki/John_Romita_Sr."},{"year":2023,"text":"Treat Williams, American actor (born 1951)","url":"https://en.wikipedia.org/wiki/Treat_Williams"},{"year":2022,"text":"Philip Baker Hall, American actor (born 1931)","url":"https://en.wikipedia.org/wiki/Philip_Baker_Hall"}]},"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."}