{"date":"2026-08-20","schema_version":1,"captured_at":"2026-08-25T23:20:30.121Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"Hayden Panettiere","views":748540,"rank":3,"extract":"Hayden Lesley Panettiere was an American actress and singer. 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Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture","authors":["Cabrera, Christian","Paleyes, Andrei","Thodoroff, Pierre","Lawrence, Neil D."],"url":"https://arxiv.org/abs/2302.04810","arxiv_id":"2302.04810","published":"2026-08-20"},{"title":"Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation","abstract":"Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including parallelization, in light of the success of machine learning in natural language processing. However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack","authors":["Chen, Le","Mahmud, Quazi Ishtiaque","Phan, Hung","Ahmed, Nesreen K.","Jannesari, Ali"],"url":"https://arxiv.org/abs/2305.05779","arxiv_id":"2305.05779","published":"2026-08-20"},{"title":"Corrections of Zipf's and Heaps' Laws Derived from Hapax Rate Models","abstract":"The article introduces corrections to Zipf's and Heaps' laws based on systematic models of the proportion of hapaxes, i.e., words that occur once. The derivation rests on two assumptions. The first one is the standard urn model which predicts that marginal frequency distributions for shorter texts look as if word tokens were sampled blindly from a given longer text. The second assumption posits that the hapax","authors":["Dębowski, Łukasz"],"url":"https://arxiv.org/abs/2307.12896","arxiv_id":"2307.12896","published":"2026-08-20"},{"title":"Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation","abstract":"Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability. We propose CLAP, a novel Conditional LAtent Point-diffusion model that combines geometric deep learning with denoising diffusion models to enhance 3D representations of the large in","authors":["Mouheb, Kaouther","Nejad, Mobina Ghojogh","Dahal, Lavsen","Samei, Ehsan","Lafata, Kyle J.","Segars, W. Paul"],"url":"https://arxiv.org/abs/2309.08289","arxiv_id":"2309.08289","published":"2026-08-20"},{"title":"Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization","abstract":"Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ev","authors":["Mitra, Pallavi","Biessmann, Felix"],"url":"https://arxiv.org/abs/2407.05788","arxiv_id":"2407.05788","published":"2026-08-20"},{"title":"The Systems Engineering Approach in Times of Large Language Models","abstract":"Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilit","authors":["Cabrera, Christian","Bastidas, Viviana","Schooling, Jennifer","Lawrence, Neil D."],"url":"https://arxiv.org/abs/2411.09050","arxiv_id":"2411.09050","published":"2026-08-20"},{"title":"OrcaLoca: An LLM Agent Framework for Software Issue Localization","abstract":"Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvements. However, localization -- precisely identifying software problems by navigating to relevant code sections -- remains a significant challenge. Current approaches often yield suboptimal results due to a lack of effective integration betwee","authors":["Yu, Zhongming","Zhang, Hejia","Zhao, Yujie","Huang, Hanxian","Yao, Matrix","Ding, Ke"],"url":"https://arxiv.org/abs/2502.00350","arxiv_id":"2502.00350","published":"2026-08-20"},{"title":"`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs","abstract":"As large language models (LLMs) are increasingly integrated into audio-based applications, growing concerns have emerged regarding their vulnerability to audio-based adversarial attacks. These systems typically follow two architectural paradigms: cascaded pipelines, where automatic speech recognition converts audio inputs into text before LLM processing, and end-to-end large audio-language models (LALMs), which direc","authors":["Huang, Linghan","Li, Bo","Chen, Huaming","Choo, Kim-Kwang Raymond"],"url":"https://arxiv.org/abs/2502.00735","arxiv_id":"2502.00735","published":"2026-08-20"},{"title":"Interactive Stroke-based Neural SDF Sculpting","abstract":"Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these representations presents challenges due to the complex relationship between model weights and surface geometry, as well as the slow optimization required to update neural fields. Among various editing tools, sculpting stands out as a valuable operation for the graphics and model","authors":["Rubab, Fizza","Tong, Yiying"],"url":"https://arxiv.org/abs/2502.02891","arxiv_id":"2502.02891","published":"2026-08-20"},{"title":"How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors","abstract":"Causal discovery methods based on the PC algorithm are proven to be sound if all structural assumptions are fulfilled and all conditional independence tests are correct. This idealized setting is rarely given in real data. In this work, we first analyze how local errors can propagate throughout the output graph of a PC-based method, highlighting how consequential seemingly innocuous errors can become. Next, we introd","authors":["Faltenbacher, Sofia","Wahl, Jonas","Herman, Rebecca","Runge, Jakob"],"url":"https://arxiv.org/abs/2502.14719","arxiv_id":"2502.14719","published":"2026-08-20"},{"title":"Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling","abstract":"Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a non-trivial feat that requires fine-tuning and can lead to so-called hallucinations, that is, the generation of images that are unrealistic. In this work, we explore image generation using flow matchi","authors":["Haber, Eldad","Ahamed, Shadab","Siddiqui, Md. Shahriar Rahim","Zakariaei, Niloufar","Eliasof, Moshe"],"url":"https://arxiv.org/abs/2502.16445","arxiv_id":"2502.16445","published":"2026-08-20"},{"title":"Using Machine Learning to Detect Fraudulent SMSs in Chichewa","abstract":"SMS enabled fraud is of great concern globally. Building classifiers based on machine learning for SMS fraud requires the use of suitable datasets for model training and validation. Most research has centred on the use of datasets of SMSs in English. This paper introduces a first dataset for SMS fraud detection in Chichewa, a major language in Africa, and reports on experiments with machine learning algorithms for cl","authors":["Taylor, Amelia","Robert, Amoss"],"url":"https://arxiv.org/abs/2502.16947","arxiv_id":"2502.16947","published":"2026-08-20"},{"title":"Contrasting Cost-Agnostic and Cost-Sensitive Losses under Limited Model Capacity via $\\mathcal H$-consistency","abstract":"There is a prevalent debate in machine learning about whether practitioners should train models to optimize a task-agnostic objective (e.g., cross entropy) or incorporate the downstream decision task into the optimization objective (e.g., weighted cross entropy). In ideal settings, like those with infinite data and infinite model capacity, the two approaches are statistically equivalent for the downstream decision ta","authors":["Finocchiaro, Jessica","Shah, Sanket","Tambe, Milind"],"url":"https://arxiv.org/abs/2502.19522","arxiv_id":"2502.19522","published":"2026-08-20"},{"title":"Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum","abstract":"With the rapid development of image synthesis techniques, AI-generated images have become increasingly realistic, which heightens the potential risk associated with their misuse and creates a growing need for reliable detection. However, the growing diversity of generative models makes it increasingly difficult for detectors to generalize to images produced by unseen generators. Most existing methods rely on artifact","authors":["Xiao, Shengpeng","Guo, Yuanfang","Peng, Heqi","Miao, Hui","Liu, Zeming","Yang, Liang"],"url":"https://arxiv.org/abs/2503.08484","arxiv_id":"2503.08484","published":"2026-08-20"},{"title":"Stack Transformer Based Spatial-Temporal Attention Model for Dynamic Sign Language and Fingerspelling Recognition","abstract":"Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. While Graph Convolutional Networks (GCNs) are common, they are limited by their reliance on fixed skeletal graphs. To overcome this, we propose the Sequential Spatio-Temporal Attention Network (SSTAN), a novel Transformer-based architecture. Our model employs a hierarchical, stacked desig","authors":["Hirooka, Koki","Miah, Abu Saleh Musa","Murakami, Tatsuya","Hasan, Md. Al Mehedi","Hwang, Yong Seok","Shin, Jungpil"],"url":"https://arxiv.org/abs/2503.16855","arxiv_id":"2503.16855","published":"2026-08-20"},{"title":"Investigating Vision-Language Model for Point Cloud-based Vehicle Classification","abstract":"Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability compared to passenger vehicles. A deeper understanding of truck characteristics is essential for enhancing the safety perspective of cooperative autonomous driving. Traditional LiDAR-based truck classification methods rely on extensive manual annotations, which makes them labor-intensive and costly. The rapid adva","authors":["Li, Yiqiao","Wei, Jie","Kamga, Camille"],"url":"https://arxiv.org/abs/2504.08154","arxiv_id":"2504.08154","published":"2026-08-20"},{"title":"The Linear Geometry of Interpretable Tokens: Jailbreaking Attacks and Defenses for Unlearned Diffusion Models","abstract":"Diffusion models excel at generating high-quality images but can memorize and reproduce harmful concepts when prompted. Although fine-tuning methods have been proposed to unlearn a target concept, they struggle to fully erase it while maintaining generation quality on other concepts, leaving models vulnerable to jailbreak attacks. Existing jailbreak methods demonstrate this vulnerability but offer limited insight int","authors":["Chen, Siyi","Zhang, Yimeng","Liu, Sijia","Qu, Qing"],"url":"https://arxiv.org/abs/2504.21307","arxiv_id":"2504.21307","published":"2026-08-20"},{"title":"Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition","abstract":"Online anomaly detection is essential in fields such as cybersecurity, healthcare, industrial monitoring, and telecommunications, where promptly identifying deviations from expected behavior can avert critical failures or security breaches. While numerous anomaly scoring methods based on supervised or unsupervised learning have been proposed, the only existing approach capable of providing assumption-free guarantees ","authors":["Farzaneh, Amirmohammad","Simeone, Osvaldo"],"url":"https://arxiv.org/abs/2505.01783","arxiv_id":"2505.01783","published":"2026-08-20"},{"title":"Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations","abstract":"We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia com","authors":["Hu, Jinming","Nawaz, Hassan","Hou, Yi-Fan","Rui, Yuting","Chi, Lijie","Chen, Yuxinxin"],"url":"https://arxiv.org/abs/2505.08195","arxiv_id":"2505.08195","published":"2026-08-20"},{"title":"A Reality Check of Language Models as Formalizers on Constraint Satisfaction Problems","abstract":"Recent work shows superior performance when using large language models (LLMs) as formalizers instead of as end-to-end solvers for symbolic reasoning problems. Given the problem description, the LLM generates a formal program that derives a solution via an external solver. We systematically investigate the formalization capability of LLMs on real-life constraint satisfaction problems on 4 benchmarks, 6 LLMs, and 2 ty","authors":["Amonkar, Rikhil","Kayan, Ceyhun Efe","Lai, Qimei","Bras, Ronan Le","Zhang, Li"],"url":"https://arxiv.org/abs/2505.13252","arxiv_id":"2505.13252","published":"2026-08-20"},{"title":"From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning","abstract":"Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities, but whether they navigate this same compression-meaning trade-off remains unclear. We apply an Information Bottleneck framework to compare human conceptual structure with embeddings from 40+ LLMs using classic categorization benchmarks. We ","authors":["Shani, Chen","Soffer, Liron","Jurafsky, Dan","LeCun, Yann","Shwartz-Ziv, Ravid"],"url":"https://arxiv.org/abs/2505.17117","arxiv_id":"2505.17117","published":"2026-08-20"},{"title":"CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting","abstract":"Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based representations, especially beyond simple color changes, remains challenging. In this work, we introduce CLIPGaussian, the first unified style transfer framework that supports text- and image-gui","authors":["Howil, Kornel","Waczyńska, Joanna","Borycki, Piotr","Dziarmaga, Tadeusz","Mazur, Marcin","Spurek, Przemysław"],"url":"https://arxiv.org/abs/2505.22854","arxiv_id":"2505.22854","published":"2026-08-20"},{"title":"Weak-to-Strong Generalization via Bregman Bias-Variance Decomposition","abstract":"Weak-to-strong generalization (W2SG) is the phenomenon in which a powerful student model, trained on labels produced by a weaker teacher, ultimately outperforms the teacher on the target task. In this work, we theoretically investigate how W2SG can arise via a generalized bias-variance decomposition under Bregman divergence. We show that the expected population risk gap between the student and the teacher is characte","authors":["Xu, Gengze","Yao, Wei","Wang, Ziqiao","Liu, Yong"],"url":"https://arxiv.org/abs/2505.24313","arxiv_id":"2505.24313","published":"2026-08-20"},{"title":"DeGLIF for Label Noise Robust Node Classification using GNNs","abstract":"Noisy labelled datasets are generally inexpensive compared to clean labelled datasets, and the same is true for graph data. In this paper, we propose a denoising technique DeGLIF: Denoising Graph Data using Leave-One-Out Influence Function. DeGLIF uses a small set of clean data and the leave-one-out influence function to make label noise robust node-level prediction on graph data. Leave-one-out influence function app","authors":["Kumar, Pintu","Hemachandra, Nandyala"],"url":"https://arxiv.org/abs/2506.00244","arxiv_id":"2506.00244","published":"2026-08-20"},{"title":"MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations","abstract":"Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these factual errors at test-time, particularly without ground-truth labels, is essential for building trustworthy autonomous agents. We propose MMD-Flagger, an hallucination detection method that utilizes Maximum Mean Discrepancy (MMD) and moni","authors":["Mitsuzawa, Kensuke","Garreau, Damien"],"url":"https://arxiv.org/abs/2506.01367","arxiv_id":"2506.01367","published":"2026-08-20"}],"history":{"events":[{"year":2018,"text":"Silent Sam, a Confederate monument on the campus of the University of North Carolina at Chapel Hill, was toppled by protestors.","url":"https://en.wikipedia.org/wiki/Silent_Sam"},{"year":2008,"text":"Spanair Flight 5022 crashed just after take-off from Madrid's Barajas Airport, killing 154 people.","url":"https://en.wikipedia.org/wiki/Spanair_Flight_5022"},{"year":1998,"text":"The Al-Shifa pharmaceutical factory (pictured) in Sudan was destroyed by a missile attack launched by the United States in retaliation for the August 7 U.S. embassy bombings.","url":"https://en.wikipedia.org/wiki/Al-Shifa_pharmaceutical_factory"},{"year":1989,"text":"After colliding with the dredger Bowbelle on the River Thames in London, the pleasure boat Marchioness sank in thirty seconds, killing 51 people.","url":"https://en.wikipedia.org/wiki/Dredging"},{"year":1989,"text":"The final stage of the O-Bahn Busway in Adelaide, South Australia, was finished, completing at the time the world's longest and fastest guided busway, with buses (example pictured) travelling 12 km (7.5 mi) at speeds of up to 100 km/h (62 mph).","url":"https://en.wikipedia.org/wiki/O-Bahn_Busway"},{"year":1988,"text":"The Troubles: The Provisional Irish Republican Army bombed a bus carrying British Army soldiers in Northern Ireland, killing eight of them and wounding twenty-eight.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1988,"text":"More than 150,000 acres (610 km2) were burned on the worst day of the largest wildfire in the recorded history of Yellowstone National Park in the United States.","url":"https://en.wikipedia.org/wiki/Yellowstone_fires_of_1988"},{"year":1950,"text":"Korean War: United Nations forces repelled an attempt by North Korea to capture the city of Taegu.","url":"https://en.wikipedia.org/wiki/Korean_War"},{"year":1920,"text":"The American Professional Football Conference, a predecessor of the National Football League, was established in Canton, Ohio.","url":"https://en.wikipedia.org/wiki/National_Football_League"},{"year":1910,"text":"Hurricane-force winds combined hundreds of small fires in the U.S. states of Washington and Idaho into the Devil's Broom fire, which burned about 4,700 square miles (12,100 km²), the largest fire in recorded U.S. history.","url":"https://en.wikipedia.org/wiki/Washington_(state)"},{"year":1909,"text":"Pluto (pictured) was photographed for the first time at the Yerkes Observatory in Williams Bay, Wisconsin, U.S., 21 years before it was officially discovered by Clyde Tombaugh.","url":"https://en.wikipedia.org/wiki/Pluto"},{"year":1892,"text":"Celtic Park, the largest football stadium in Scotland and home of Celtic F.C., opened.","url":"https://en.wikipedia.org/wiki/Celtic_Park"},{"year":1710,"text":"War of the Spanish Succession: A Spanish Bourbon army commanded by the Marquis de Bay was soundly defeated by a multinational army led by the Austrian commander Guido Starhemberg.","url":"https://en.wikipedia.org/wiki/War_of_the_Spanish_Succession"},{"year":1707,"text":"The first Siege of Pensacola came to an end with the British and Creek abandoning their attempt to capture Pensacola in Spanish Florida.","url":"https://en.wikipedia.org/wiki/Siege_of_Pensacola_(1707)"},{"year":917,"text":"Byzantine–Bulgarian wars: Bulgarian forces led by Tsar Simeon I drove the Byzantines out of Thrace with a decisive victory at the Battle of Achelous.","url":"https://en.wikipedia.org/wiki/Byzantine%E2%80%93Bulgarian_wars"}],"births":[{"year":2003,"text":"Prince Gabriel of Belgium","url":"https://en.wikipedia.org/wiki/Prince_Gabriel_of_Belgium"},{"year":1998,"text":"Lieke Klaver, Dutch track and field athlete","url":"https://en.wikipedia.org/wiki/Lieke_Klaver"},{"year":1997,"text":"Kaho Minagawa, Japanese rhythmic gymnast","url":"https://en.wikipedia.org/wiki/Kaho_Minagawa"},{"year":1997,"text":"Daniel Vladař, Czech ice hockey player","url":"https://en.wikipedia.org/wiki/Daniel_Vlada%C5%99"},{"year":1996,"text":"Bunty Afoa, New Zealand rugby league player","url":"https://en.wikipedia.org/wiki/Bunty_Afoa"},{"year":1995,"text":"Liana Liberato, American actress","url":"https://en.wikipedia.org/wiki/Liana_Liberato"},{"year":1994,"text":"Mitchell Trubisky, American football player","url":"https://en.wikipedia.org/wiki/Mitchell_Trubisky"},{"year":1993,"text":"Tonisha Rock-Yaw, Barbadian netball player","url":"https://en.wikipedia.org/wiki/Tonisha_Rock-Yaw"}],"deaths":[{"year":2024,"text":"Al Attles, American basketball player and coach (born 1936)","url":"https://en.wikipedia.org/wiki/Al_Attles"},{"year":2022,"text":"Darya Dugina, Russian journalist (born 1992)","url":"https://en.wikipedia.org/wiki/Darya_Dugina"},{"year":2021,"text":"Igor Vovkovinskiy, Ukrainian-American law student and actor, American tallest person (born 1982)","url":"https://en.wikipedia.org/wiki/Igor_Vovkovinskiy"},{"year":2020,"text":"Chi Chi DeVayne, American Drag Queen (born 1985)","url":"https://en.wikipedia.org/wiki/Chi_Chi_DeVayne"},{"year":2018,"text":"Uri Avnery, Israeli writer, politician and peace activist (born 1923)","url":"https://en.wikipedia.org/wiki/Uri_Avnery"},{"year":2018,"text":"Jennifer Ramírez Rivero, Venezuelan model (born 1978)","url":"https://en.wikipedia.org/wiki/Jennifer_Ram%C3%ADrez_Rivero"},{"year":2017,"text":"Jerry Lewis, American actor and comedian (born 1926)","url":"https://en.wikipedia.org/wiki/Jerry_Lewis"},{"year":2015,"text":"Egon Bahr, German journalist and politician, Federal Minister for Special Affairs of Germany (born 1922)","url":"https://en.wikipedia.org/wiki/Egon_Bahr"}]},"sources":[{"id":"attention","name":"Wikipedia 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Every item links to its source."}