{"date":"2026-08-12","schema_version":1,"captured_at":"2026-08-25T23:19:32.070Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"Spider-Man: Brand New Day","views":286897,"rank":5,"extract":"Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. 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We present evidence that DNNs are capable of generalizing to objects in novel orientations by disseminating orientation-invariance obtained from familiar objects seen from many viewpoints. This capability strengthens when training the DNN with an increasing number of","authors":["Cooper, Avi","Boix, Xavier","Harari, Daniel","Madan, Spandan","Pfister, Hanspeter","Sasaki, Tomotake"],"url":"https://arxiv.org/abs/2109.13445","arxiv_id":"2109.13445","published":"2026-08-12"},{"title":"Representation and Invariance in Reinforcement Learning","abstract":"Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework. In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks. We introduce a criterion which is sufficient for relative intellige","authors":["Alexander, Samuel","Pedersen, Arthur Paul"],"url":"https://arxiv.org/abs/2112.07752","arxiv_id":"2112.07752","published":"2026-08-12"},{"title":"From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms","abstract":"Estimation-of-distribution algorithms (EDAs) are optimization algorithms that learn a distribution on the search space from which good solutions can be sampled easily. A key parameter of most EDAs is the sample size (population size). If the population size is too small, the update of the probabilistic model builds on few samples, leading to the undesired effect of genetic drift. Too large population sizes avoid gene","authors":["Zheng, Weijie","Doerr, Benjamin"],"url":"https://arxiv.org/abs/2206.09090","arxiv_id":"2206.09090","published":"2026-08-12"},{"title":"Graphical Models of False Information and Fact Checking Ecosystems","abstract":"The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised and globalised society. A lot of research has been done to better understand different aspects of false information online such as behaviours of different actors and patterns of spreading, and also on better detection and prevention of such information using technical and socio","authors":["Yuan, Haiyue","Altuncu, Enes","Li, Shujun","Baskent, Can","Nurse, Jason R. C."],"url":"https://arxiv.org/abs/2208.11582","arxiv_id":"2208.11582","published":"2026-08-12"},{"title":"Robust, randomized preconditioning for kernel ridge regression","abstract":"We investigate preconditioned conjugate gradient methods for kernel ridge regression (KRR) problems with a moderate to large number of data points ($10^4 \\leq N \\leq 10^7$). We develop and analyze two randomized preconditioners with complementary guarantees. For full-data KRR, RPCholesky preconditioning requires $O(N^2)$ arithmetic operations to achieve fixed accuracy under sufficiently rapid eigenvalue decay of the ","authors":["Díaz, Mateo","Epperly, Ethan N.","Frangella, Zachary","Tropp, Joel A.","Webber, Robert J."],"url":"https://arxiv.org/abs/2304.12465","arxiv_id":"2304.12465","published":"2026-08-12"},{"title":"Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits","abstract":"In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator. Because WRLS only provides point estimates, previous methods typically construct surrogate distributions when aiming to perform Bayesian-like randomized exploration. To more properly establish the Bayesian principles, we introduce Weighted Sequential Bayesian (WSB) infe","authors":["Werge, Nicklas","Wu, Yi-Shan","Akgül, Abdullah","Kandemir, Melih"],"url":"https://arxiv.org/abs/2307.03587","arxiv_id":"2307.03587","published":"2026-08-12"},{"title":"A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning","abstract":"Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically provable bounds to protect such data, previous work has focused almost exclusively on unweighted ERM. We consider weighted ERM (wERM) -- an important generalization where individual contributions to the objective function vary. We propose the ","authors":["Giddens, Spencer","Zhou, Yiwang","Krull, Kevin R.","Brinkman, Tara M.","Song, Peter X. K.","Liu, Fang"],"url":"https://arxiv.org/abs/2307.13127","arxiv_id":"2307.13127","published":"2026-08-12"},{"title":"Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization","abstract":"signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients. Several standard analyses assume independent stochastic-gradient samples, whereas a common finite-sum implementation reshuffles the data and processes them sequentially. We study this variant, signSGD with random reshuffling (SignRR), and show that reshuffling does not in general repair the bias c","authors":["Qin, Zhen","Liu, Zhishuai","Xu, Pan"],"url":"https://arxiv.org/abs/2310.15976","arxiv_id":"2310.15976","published":"2026-08-12"},{"title":"Pretrained Optimization Model for Zero-Shot Black Box Optimization","abstract":"Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable and robust performance in various applications. Current optimizers often struggle with zero-shot optimization and require intricate hyperparameter tuning to adapt to new tasks. To address this, we propo","authors":["Li, Xiaobin","Wu, Kai","Li, Yujian Betterest","Zhang, Xiaoyu","Wang, Handing","Liu, Jing"],"url":"https://arxiv.org/abs/2405.03728","arxiv_id":"2405.03728","published":"2026-08-12"},{"title":"ZeroPur: Succinct Training-Free Adversarial Purification","abstract":"Adversarial purification is a kind of defense technique that can defend against various unseen adversarial attacks without modifying the victim classifier. Existing methods often depend on external generative models or cooperation between auxiliary functions and victim classifiers. However, retraining generative models, auxiliary functions, or victim classifiers relies on the domain of the fine-tuned dataset and is c","authors":["Liu, Erhu","Yang, Zonglin","Liu, Bo","Meng, Xianjia","Bi, Xiuli","Han, Junwei"],"url":"https://arxiv.org/abs/2406.03143","arxiv_id":"2406.03143","published":"2026-08-12"},{"title":"A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression","abstract":"We propose a scalable variational Bayes method for statistical inference for a single or pre-specified low-dimensional subset of the coordinates of a high-dimensional parameter in sparse linear regression. Our approach relies on assigning a mean-field approximation to the nuisance coordinates and carefully modelling the conditional distribution of the target given the nuisance. This requires only a preprocessing step","authors":["Castillo, Ismaël","L'Huillier, Alice","Ray, Kolyan","Travis, Luke"],"url":"https://arxiv.org/abs/2406.12659","arxiv_id":"2406.12659","published":"2026-08-12"},{"title":"CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks","abstract":"Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in va","authors":["Deng, Yongxin","Qiu, Xihe","Tan, Xiaoyu","Qu, Chao","Pan, Jing","Cheng, Yuan"],"url":"https://arxiv.org/abs/2409.03381","arxiv_id":"2409.03381","published":"2026-08-12"},{"title":"Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework","abstract":"The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is crucial for optimizing computational resources to accurately estimate expected rewards for the agent. In scenarios with sparse rewards, such as robotic control systems, achieving this balance is particularly challenging. However, given that","authors":["Deng, Yongxin","Qiu, Xihe","Chen, Jue","Tan, Xiaoyu"],"url":"https://arxiv.org/abs/2409.04744","arxiv_id":"2409.04744","published":"2026-08-12"},{"title":"ADIOS: Antibody Development via Opponent Shaping","abstract":"Anti-viral therapies are typically designed to target only the current strains of a virus, a myopic response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both defend against and actively influence viral evolution. This motivates our met","authors":["Towers, Sebastian","Kalisz, Aleksandra","Robert, Philippe A.","Higueruelo, Alicia","Vianello, Francesca","Tsai, Ming-Han Chloe"],"url":"https://arxiv.org/abs/2409.10588","arxiv_id":"2409.10588","published":"2026-08-12"},{"title":"FedSlate:A Federated Deep Reinforcement Learning Recommender System","abstract":"Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems do not fully exploit the relevance of individual user behavior across different platforms. One potential solution is to aggregate data from various platforms in a centralized location and use the aggregated data for training. However, this","authors":["Deng, Yongxin","Qiu, Xihe","Tan, Xiaoyu","Jin, Yaochu"],"url":"https://arxiv.org/abs/2409.14872","arxiv_id":"2409.14872","published":"2026-08-12"},{"title":"Predicting total time to compress a video corpus using online inference systems","abstract":"Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compression time, and thus estimating the cost of video compression. ","authors":["Shu, Xin","Vibhoothi, Vibhoothi","Kokaram, Anil"],"url":"https://arxiv.org/abs/2410.18260","arxiv_id":"2410.18260","published":"2026-08-12"},{"title":"Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning","abstract":"Traffic simulations are commonly used to optimize urban traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control, particularly in intelligent transportation systems involving connected automated vehicles. Multi-agent reinforcement learning (MARL) is particularly effective for learning control strategies for traffic lights in a network using iterative simulations.","authors":["Azfar, Talha","Huang, Kaicong","Tracy, Andrew","Misiewicz, Sandra","Liu, Chenxi","Ke, Ruimin"],"url":"https://arxiv.org/abs/2412.03925","arxiv_id":"2412.03925","published":"2026-08-12"},{"title":"Regression and Classification with Single-Qubit Quantum Neural Networks","abstract":"The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other. Motivated by the rich connection between these areas, we use a resource-efficient and scalable Single-Qubit Quantum Neural Network (SQQNN) for both regression and classification tasks using a new data uploading technique. The SQQNN leverage","authors":["Souza, Leandro C.","Guingo, Bruno C.","Giraldi, Gilson","Portugal, Renato"],"url":"https://arxiv.org/abs/2412.09486","arxiv_id":"2412.09486","published":"2026-08-12"},{"title":"KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning","abstract":"This paper proposes a novel learning approach for designing Kazantzis-Kravaris or nonlinear Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer's state is then mapped back to the original system coordinates via the inve","authors":["Niazi, M. Umar B.","Cao, John","Barreau, Matthieu","Johansson, Karl Henrik"],"url":"https://arxiv.org/abs/2501.11655","arxiv_id":"2501.11655","published":"2026-08-12"},{"title":"Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign","abstract":"This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to revea","authors":["Zhang, Ruisi","Javidnia, Neusha","Sheybani, Nojan","Koushanfar, Farinaz"],"url":"https://arxiv.org/abs/2502.02068","arxiv_id":"2502.02068","published":"2026-08-12"},{"title":"Quantum-aware Transformer model for state classification","abstract":"Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural ne","authors":["Sekuła, Przemysław","Romaszewski, Michał","Głomb, Przemysław","Cholewa, Michał","Pawela, Łukasz"],"url":"https://arxiv.org/abs/2502.21055","arxiv_id":"2502.21055","published":"2026-08-12"},{"title":"No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding","abstract":"Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-Judge framework, which uses prompted LLMs to evaluate response quality, is appealing due to its scalability, low cost, and strong correlations with human stylistic preferences. However, it remains unclear how accurately these methods can as","authors":["Krumdick, Michael","Lovering, Charles","Reddy, Varshini","Ebner, Seth","Tanner, Chris"],"url":"https://arxiv.org/abs/2503.05061","arxiv_id":"2503.05061","published":"2026-08-12"},{"title":"GranQ: Efficient Channel-wise Quantization via Vectorized Pre-Scaling for Zero-Shot QAT","abstract":"Zero-shot quantization (ZSQ) enables neural network compression without original training data, making it a promising solution for restricted data access scenarios. To compensate for the lack of data, recent ZSQ methods typically rely on synthetic inputs generated from the full-precision model. However, these synthetic inputs often lead to activation distortion, especially under low-bit settings. To mitigate this, ex","authors":["Hong, Inpyo","Jo, Youngwan","Lee, Hyojeong","Ahn, Sunghyun","Lee, Kijung","Park, Sanghyun"],"url":"https://arxiv.org/abs/2503.18339","arxiv_id":"2503.18339","published":"2026-08-12"},{"title":"Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking","abstract":"Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized imitation of high-value creative works. Existing copyright protection techniques mainly focus on visual media, leaving the protection of creative writing largely unexplored. In this work, we investigate a new challenge: verifying whether AI-","authors":["Zhang, Ziwei","Wen, Juan","Peng, Wanli","Wu, Zhengxian","Zhou, Yinghan","Xue, Yiming"],"url":"https://arxiv.org/abs/2504.00035","arxiv_id":"2504.00035","published":"2026-08-12"},{"title":"Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting","abstract":"Accurate LiDAR-camera calibration is crucial for multi-sensor systems. However, traditional methods often rely on physical targets, which are impractical for real-world deployment. Moreover, even carefully calibrated extrinsics can degrade over time due to sensor drift or external disturbances, necessitating periodic recalibration. To address these challenges, we present a Targetless LiDAR-Camera Calibration (TLC-Cal","authors":["Jung, Haebeom","Kim, Namtae","Kim, Jungwoo","Park, Jaesik"],"url":"https://arxiv.org/abs/2504.04597","arxiv_id":"2504.04597","published":"2026-08-12"}],"history":{"events":[{"year":2021,"text":"Six people, including the perpetrator, were killed in a mass shooting in Plymouth, England.","url":"https://en.wikipedia.org/wiki/Plymouth_shooting"},{"year":2016,"text":"The state-owned Taedonggang Brewing Company inaugurated the first beer festival in North Korea.","url":"https://en.wikipedia.org/wiki/Taedonggang_Brewing_Company"},{"year":2000,"text":"Kursk, an Oscar-class submarine of the Russian Navy, suffered an on-board explosion and sank in the Barents Sea during a military exercise, killing 118 people.","url":"https://en.wikipedia.org/wiki/Russian_submarine_Kursk"},{"year":1990,"text":"Near Faith, South Dakota, American paleontologist Sue Hendrickson found one of the most complete Tyrannosaurus rex skeletons, nicknamed Sue (pictured).","url":"https://en.wikipedia.org/wiki/Faith%2C_South_Dakota"},{"year":1985,"text":"Japan Air Lines Flight 123 (aircraft involved pictured) crashed into Mount Takamagahara in Japan, killing 520 of 524 people on board, including singer Kyu Sakamoto, in the world's deadliest single-aircraft aviation disaster.","url":"https://en.wikipedia.org/wiki/Japan_Air_Lines_Flight_123"},{"year":1969,"text":"The Troubles: Riots erupted in the neighbourhood of Bogside in Derry, and spread across much of Northern Ireland.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1952,"text":"Thirteen Jewish poets were executed in Moscow for espionage based on false confessions.","url":"https://en.wikipedia.org/wiki/Night_of_the_Murdered_Poets"},{"year":1945,"text":"An official administrative history of the Manhattan Project, written by American physicist Henry DeWolf Smyth, was released to the public days after the atomic bombings of Hiroshima and Nagasaki.","url":"https://en.wikipedia.org/wiki/Smyth_Report"},{"year":1944,"text":"World War II: In Sant'Anna di Stazzema, Italy, the Waffen-SS and the Brigate Nere murdered about 560 local villagers and refugees and burned their bodies.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1914,"text":"World War I: Belgian troops won a victory at the Battle of Halen, but were ultimately unable to stop the German invasion of Belgium.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1883,"text":"The last known quagga (example pictured), a subspecies of the plains zebra, died at Natura Artis Magistra, a zoo in Amsterdam.","url":"https://en.wikipedia.org/wiki/Quagga"},{"year":1881,"text":"Franco-American children as young as seven years old began striking against Cabot Mill in Brunswick, Maine, shutting down operations for three days.","url":"https://en.wikipedia.org/wiki/French_Americans"},{"year":1834,"text":"A race riot in Philadelphia, United States, destroyed African-American businesses and killed two people.","url":"https://en.wikipedia.org/wiki/1834_Philadelphia_race_riot"},{"year":1323,"text":"Sweden and the Novgorod Republic signed the Treaty of Nöteborg, resulting in a hiatus in the Swedish–Novgorodian Wars.","url":"https://en.wikipedia.org/wiki/Novgorod_Republic"},{"year":1099,"text":"Crusades: Fatimid forces under al-Afdal Shahanshah began retreating to Egypt after the Battle of Ascalon, concluding the First Crusade.","url":"https://en.wikipedia.org/wiki/Crusades"}],"births":[{"year":2001,"text":"Dixie D'Amelio, American social media personality and singer","url":"https://en.wikipedia.org/wiki/Dixie_D'Amelio"},{"year":2000,"text":"Tristan Charpentier, French racing driver","url":"https://en.wikipedia.org/wiki/Tristan_Charpentier"},{"year":1999,"text":"Matthijs de Ligt, Dutch footballer","url":"https://en.wikipedia.org/wiki/Matthijs_de_Ligt"},{"year":1999,"text":"Dream, American YouTuber","url":"https://en.wikipedia.org/wiki/Dream_(YouTuber)"},{"year":1999,"text":"Jule Niemeier, German tennis player","url":"https://en.wikipedia.org/wiki/Jule_Niemeier"},{"year":1999,"text":"GK Barry, social media star and presenter","url":"https://en.wikipedia.org/wiki/GK_Barry"},{"year":1998,"text":"Stefanos Tsitsipas, Greek tennis player","url":"https://en.wikipedia.org/wiki/Stefanos_Tsitsipas"},{"year":1996,"text":"Choi Yu-jin, South Korean singer and actress","url":"https://en.wikipedia.org/wiki/Choi_Yu-jin"}],"deaths":[{"year":2024,"text":"Kim Kahana, American actor and stunt performer (born 1929)","url":"https://en.wikipedia.org/wiki/Kim_Kahana"},{"year":2021,"text":"Una Stubbs, English actress, TV personality, and dancer (born 1937)","url":"https://en.wikipedia.org/wiki/Una_Stubbs"},{"year":2020,"text":"Bill Yeoman, American college football player and coach (born 1927)","url":"https://en.wikipedia.org/wiki/Bill_Yeoman"},{"year":2019,"text":"DJ Arafat, Ivorian DJ and singer (born 1986)","url":"https://en.wikipedia.org/wiki/DJ_Arafat"},{"year":2017,"text":"Bryan Murray, Canadian ice hockey coach (born 1942)","url":"https://en.wikipedia.org/wiki/Bryan_Murray_(ice_hockey)"},{"year":2016,"text":"Juan Pedro de Miguel, Spanish handball player (born 1958)","url":"https://en.wikipedia.org/wiki/Juan_Pedro_de_Miguel"},{"year":2015,"text":"Jaakko Hintikka, Finnish philosopher and academic (born 1929)","url":"https://en.wikipedia.org/wiki/Jaakko_Hintikka"},{"year":2015,"text":"Stephen Lewis, English actor and screenwriter (born 1926)","url":"https://en.wikipedia.org/wiki/Stephen_Lewis_(actor)"}]},"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."}