{"date":"2025-11-01","schema_version":1,"captured_at":"2026-08-25T22:55:37.201Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":12},"attention":[{"title":"Andrew Mountbatten-Windsor","views":378085,"rank":3,"extract":"Andrew Albert Christian Edward Mountbatten-Windsor, formerly Prince Andrew, Duke of York, is the third child and second son of Queen Elizabeth II and Prince Philip, Duke of Edinburgh, and a younger brother of King Charles III. 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Due to diverse task execution need, such serving heavily rely on heterogeneous CPU-GPU systems with majority of the external tools responsible for agentic capability, either run on or are orchestrated by the CPU. Towards having a deeper understanding of its role,","authors":["Ritik Raj","Souvik Kundu","Ishita Vohra","Hong Wang","Tushar Krishna"],"url":"http://arxiv.org/abs/2511.00739v3","arxiv_id":"2511.00739v3","published":"2025-11-01T23:46:44Z"},{"title":"EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inference","abstract":"While homomorphic encryption (HE) provides strong privacy protection, its high computational cost has restricted its application to simple tasks. Recently, hyperdimensional computing (HDC) applied to HE has shown promising performance for privacy-preserving machine learning (PPML). However, when applied to more realistic scenarios such as batch inference, the HDC-based HE has still very high compute time as well as h","authors":["Jaewoo Park","Chenghao Quan","Jongeun Lee"],"url":"http://arxiv.org/abs/2511.00737v1","arxiv_id":"2511.00737v1","published":"2025-11-01T23:22:01Z"},{"title":"FeNN-DMA: A RISC-V SoC for SNN acceleration","abstract":"Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video classification. However, SNNs have a much lower arithmetic intensity than ANNs and are therefore not well-matched to standard accelerators like GPUs and TPUs. Field Programmable Gate Arrays (FPGAs) are de","authors":["Zainab Aizaz","James C. Knight","Thomas Nowotny"],"url":"http://arxiv.org/abs/2511.00732v2","arxiv_id":"2511.00732v2","published":"2025-11-01T22:59:54Z"},{"title":"Fibbinary-Based Compression and Quantization for Efficient Neural Radio Receivers","abstract":"Neural receivers have shown outstanding performance compared to the conventional ones but this comes with a high network complexity leading to a heavy computational cost. This poses significant challenges in their deployment on hardware-constrained devices. To address the issue, this paper explores two optimization strategies: quantization and compression. We introduce both uniform and non-uniform quantization such a","authors":["Roberta Fiandaca","Manil Dev Gomony"],"url":"http://arxiv.org/abs/2511.01921v1","arxiv_id":"2511.01921v1","published":"2025-11-01T22:39:44Z"},{"title":"TRISKELION-1: Unified Descriptive-Predictive-Generative AI","abstract":"TRISKELION-1 is a unified descriptive-predictive-generative architecture that integrates statistical, mechanistic, and generative reasoning within a single encoder-decoder framework. The model demonstrates how descriptive representation learning, predictive inference, and generative synthesis can be jointly optimized using variational objectives. Experiments on MNIST validate that descriptive reconstruction, predicti","authors":["Nardeep Kumar","Arun Kanwar"],"url":"http://arxiv.org/abs/2511.00711v1","arxiv_id":"2511.00711v1","published":"2025-11-01T21:23:38Z"},{"title":"Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models","abstract":"Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities, but these insights are predominantly limited to language-only domains, leaving the dynamics of visual-centric spatial reasoning under-explored. To examine the impact of RLVR on the capability boundaries of Vision-Language Models ","authors":["Minghe Shen","Zhuo Zhi","Chonghan Liu","Shuo Xing","Zhengzhong Tu","Che Liu"],"url":"http://arxiv.org/abs/2511.00710v4","arxiv_id":"2511.00710v4","published":"2025-11-01T21:19:41Z"},{"title":"A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment","abstract":"Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities, which can impact data quality in clinical trials. To address this gap, we introduce a voice-enabled virtual patient simulation system powered by a large language model (LLM). This study describes the system's development and validates its ability to generate virtua","authors":["Veronica Bossio Botero","Vijay Yadav","Jacob Ouyang","Anzar Abbas","Michelle Worthington"],"url":"http://arxiv.org/abs/2511.00709v2","arxiv_id":"2511.00709v2","published":"2025-11-01T21:18:08Z"},{"title":"Agentic Educational Content Generation for African Languages on Edge Devices","abstract":"Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demo","authors":["Ravi Gupta","Guneet Bhatia"],"url":"http://arxiv.org/abs/2511.07437v1","arxiv_id":"2511.07437v1","published":"2025-11-01T21:13:47Z"},{"title":"Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation","abstract":"Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or specialized information. A natural strategy to increase the likelihood of retrieving relevant information is to expand the number of retrieved documents. However, involving more documents could introduce significant noise, as many documents may be","authors":["Song Wang","Zihan Chen","Peng Wang","Zhepei Wei","Zhen Tan","Yu Meng"],"url":"http://arxiv.org/abs/2511.04700v1","arxiv_id":"2511.04700v1","published":"2025-11-01T20:08:13Z"},{"title":"Evolve to Inspire: Novelty Search for Diverse Image Generation","abstract":"Text-to-image diffusion models, while proficient at generating high-fidelity images, often suffer from limited output diversity, hindering their application in exploratory and ideation tasks. Existing prompt optimization techniques typically target aesthetic fitness or are ill-suited to the creative visual domain. To address this shortcoming, we introduce WANDER, a novelty search-based approach to generating diverse ","authors":["Alex Inch","Passawis Chaiyapattanaporn","Yuchen Zhu","Yuan Lu","Ting-Wen Ko","Davide Paglieri"],"url":"http://arxiv.org/abs/2511.00686v1","arxiv_id":"2511.00686v1","published":"2025-11-01T19:58:07Z"},{"title":"Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control","abstract":"Magnetic Resonance Imaging suffers from substantial data heterogeneity and the absence of standardized contrast labels across scanners, protocols, and institutions, which severely limits large-scale automated analysis. A unified representation of MRI contrast would enable a wide range of downstream utilities, from automatic sequence recognition to harmonization and quality control, without relying on manual annotatio","authors":["Mehmet Yigit Avci","Pedro Borges","Virginia Fernandez","Paul Wright","Mehmet Yigitsoy","Sebastien Ourselin"],"url":"http://arxiv.org/abs/2511.00681v1","arxiv_id":"2511.00681v1","published":"2025-11-01T19:49:32Z"},{"title":"Isotropic Curvature Model for Understanding Deep Learning Optimization: Is Gradient Orthogonalization Optimal?","abstract":"In this paper, we introduce a model for analyzing deep learning optimization over a single iteration by leveraging the matrix structure of the weights. We derive the model by assuming isotropy of curvature, including the second-order Hessian and higher-order terms, of the loss function across all perturbation directions; hence, we call it the isotropic curvature model. This model is a convex optimization program amen","authors":["Weijie Su"],"url":"http://arxiv.org/abs/2511.00674v1","arxiv_id":"2511.00674v1","published":"2025-11-01T19:37:29Z"},{"title":"Lifted Successor Generation in Numeric Planning","abstract":"Most planners ground numeric planning tasks, given in a first-order-like language, into a ground task representation. However, this can lead to an exponential blowup in task representation size, which occurs in practice for hard-to-ground tasks. We extend a state-of-the-art lifted successor generator for classical planning to support numeric precondition applicability. The method enumerates maximum cliques in a subst","authors":["Dominik Drexler"],"url":"http://arxiv.org/abs/2511.00673v1","arxiv_id":"2511.00673v1","published":"2025-11-01T19:33:16Z"},{"title":"ShadowLogic: Backdoors in Any Whitebox LLM","abstract":"Large language models (LLMs) are widely deployed across various applications, often with safeguards to prevent the generation of harmful or restricted content. However, these safeguards can be covertly bypassed through adversarial modifications to the computational graph of a model. This work highlights a critical security vulnerability in computational graph-based LLM formats, demonstrating that widely used deployme","authors":["Kasimir Schulz","Amelia Kawasaki","Leo Ring"],"url":"http://arxiv.org/abs/2511.00664v1","arxiv_id":"2511.00664v1","published":"2025-11-01T19:10:08Z"},{"title":"Automated Invoice Data Extraction: Using LLM and OCR","abstract":"Conventional Optical Character Recognition (OCR) systems are challenged by variant invoice layouts, handwritten text, and low-quality scans, which are often caused by strong template dependencies that restrict their flexibility across different document structures and layouts. Newer solutions utilize advanced deep learning models such as Convolutional Neural Networks (CNN) as well as Transformers, and domain-specific","authors":["Khushi Khanchandani","Advait Thakur","Akshita Shetty","Chaitravi Reddy","Ritisa Behera"],"url":"http://arxiv.org/abs/2511.05547v2","arxiv_id":"2511.05547v2","published":"2025-11-01T19:05:09Z"},{"title":"Lessons Learned from the Use of Generative AI in Engineering and Quality Assurance of a WEB System for Healthcare","abstract":"The advances and availability of technologies involving Generative Artificial Intelligence (AI) are evolving clearly and explicitly, driving immediate changes in various work activities. Software Engineering (SE) is no exception and stands to benefit from these new technologies, enhancing productivity and quality in its software development processes. However, although the use of Generative AI in SE practices is stil","authors":["Guilherme H. Travassos","Sabrina Rocha","Rodrigo Feitosa","Felipe Assis","Patricia Goncalves","Andre Gheventer"],"url":"http://arxiv.org/abs/2511.00658v1","arxiv_id":"2511.00658v1","published":"2025-11-01T18:42:29Z"},{"title":"Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective","abstract":"With this paper, we survey techniques for improving the predictive accuracy of pretrained large language models by allocating additional compute at inference time. In categorizing test-time scaling methods, we place special emphasis on how a problem is decomposed into subproblems and on the topological organization of these subproblems whether sequential, parallel, or tree-structured. This perspective allows us to un","authors":["Zhuoyi Yang","Xu Guo","Tong Zhang","Huijuan Xu","Boyang Li"],"url":"http://arxiv.org/abs/2511.14772v1","arxiv_id":"2511.14772v1","published":"2025-11-01T18:41:23Z"},{"title":"Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting","abstract":"Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to many telecom tasks, existing models are often narrow in scope, require large amounts of labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting continues to rel","authors":["Chenhua Shi","Bhavika Jalli","Gregor Macdonald","John Zou","Wanlu Lei","Mridul Jain"],"url":"http://arxiv.org/abs/2511.00651v2","arxiv_id":"2511.00651v2","published":"2025-11-01T18:19:41Z"},{"title":"More Than A Shortcut: A Hyperbolic Approach To Early-Exit Networks","abstract":"Deploying accurate event detection on resource-constrained devices is challenged by the trade-off between performance and computational cost. While Early-Exit (EE) networks offer a solution through adaptive computation, they often fail to enforce a coherent hierarchical structure, limiting the reliability of their early predictions. To address this, we propose Hyperbolic Early-Exit networks (HypEE), a novel framework","authors":["Swapnil Bhosale","Cosmin Frateanu","Camilla Clark","Arnoldas Jasonas","Chris Mitchell","Xiatian Zhu"],"url":"http://arxiv.org/abs/2511.00641v1","arxiv_id":"2511.00641v1","published":"2025-11-01T17:43:02Z"},{"title":"DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching","abstract":"Large Reasoning Models (LRMs) achieve remarkable inference-time improvements through parallel thinking. However, existing methods rely on redundant sampling of reasoning trajectories, failing to effectively explore the reasoning space to uncover high-quality solutions. To address these limitations, we propose Decoding Tree Sketching (DTS), a plug-and-play decoding framework for structural multi-trajectory exploration","authors":["Zicheng Xu","Xiuyi Lou","Guanchu Wang","Yu-Neng Chuang","Feng Luo","Guangyao Zheng"],"url":"http://arxiv.org/abs/2511.00640v2","arxiv_id":"2511.00640v2","published":"2025-11-01T17:41:28Z"},{"title":"Node Preservation and its Effect on Crossover in Cartesian Genetic Programming","abstract":"While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone $(1+λ)$ evolutionary strategy has become the canonical approach for CGP. Although several operators have been developed that demonstrate an increased performance over the can","authors":["Mark Kocherovsky","Illya Bakurov","Wolfgang Banzhaf"],"url":"http://arxiv.org/abs/2511.00634v2","arxiv_id":"2511.00634v2","published":"2025-11-01T17:26:56Z"},{"title":"AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systems","abstract":"With the rapid progress of large language models (LLMs), LLM-powered multi-agent systems (MAS) are drawing increasing interest across academia and industry. However, many current MAS frameworks struggle with reliability and scalability, especially on complex tasks. We present AgentGit, a framework that brings Git-like rollback and branching to MAS workflows. Built as an infrastructure layer on top of LangGraph, Agent","authors":["Yang Li","Siqi Ping","Xiyu Chen","Xiaojian Qi","Zigan Wang","Ye Luo"],"url":"http://arxiv.org/abs/2511.00628v1","arxiv_id":"2511.00628v1","published":"2025-11-01T17:11:31Z"},{"title":"Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering","abstract":"Large language models (LLMs) can be controlled at inference time through prompts (in-context learning) and internal activations (activation steering). Different accounts have been proposed to explain these methods, yet their common goal of controlling model behavior raises the question of whether these seemingly disparate methodologies can be seen as specific instances of a broader framework. Motivated by this, we de","authors":["Eric Bigelow","Daniel Wurgaft","YingQiao Wang","Noah Goodman","Tomer Ullman","Hidenori Tanaka"],"url":"http://arxiv.org/abs/2511.00617v2","arxiv_id":"2511.00617v2","published":"2025-11-01T16:46:03Z"},{"title":"PreferThinker: Reasoning-based Personalized Image Preference Assessment","abstract":"Personalized image preference assessment aims to evaluate an individual user's image preferences by relying only on a small set of reference images as prior information. Existing methods mainly focus on general preference assessment, training models with large-scale data to tackle well-defined tasks such as text-image alignment. However, these approaches struggle to handle personalized preference because user-specifi","authors":["Shengqi Xu","Xinpeng Zhou","Yabo Zhang","Ming Liu","Tao Liang","Tianyu Zhang"],"url":"http://arxiv.org/abs/2511.00609v4","arxiv_id":"2511.00609v4","published":"2025-11-01T16:19:51Z"},{"title":"EPARA: Parallelizing Categorized AI Inference in Edge Clouds","abstract":"With the increasing adoption of AI applications such as large language models and computer vision AI, the computational demands on AI inference systems are continuously rising, making the enhancement of task processing capacity using existing hardware a primary objective in edge clouds. We propose EPARA, an end-to-end AI parallel inference framework in edge, aimed at enhancing the edge AI serving capability. Our key ","authors":["Yubo Wang","Yubo Cui","Tuo Shi","Danyang Li","Wenxin Li","Lide Suo"],"url":"http://arxiv.org/abs/2511.00603v1","arxiv_id":"2511.00603v1","published":"2025-11-01T16:09:14Z"}],"history":{"events":[{"year":1972,"text":"Elvis on Tour, a concert film that documented Elvis Presley's tour throughout the United States, opened.","url":"https://en.wikipedia.org/wiki/Elvis_on_Tour"},{"year":1963,"text":"Lê Quang Tung, loyalist head of the South Vietnam Special Forces, was executed in a U.S.-backed coup against president Ngô Đình Diệm following a period of religious unrest.","url":"https://en.wikipedia.org/wiki/L%C3%AA_Quang_Tung"},{"year":1959,"text":"After being struck in the face with a hockey puck, Jacques Plante played the rest of the game wearing a face mask, now mandatory equipment for goaltenders in ice hockey.","url":"https://en.wikipedia.org/wiki/Hockey_puck"},{"year":1959,"text":"Dominique Mbonyumutwa, one of the few Hutu sub-chiefs in colonial Rwanda, was attacked by Tutsi activists, precipitating the Rwandan Revolution.","url":"https://en.wikipedia.org/wiki/Dominique_Mbonyumutwa"},{"year":1956,"text":"The Indian states Kerala, Andhra Pradesh, and Karnataka were formally created under the States Reorganisation Act.","url":"https://en.wikipedia.org/wiki/Kerala"},{"year":1944,"text":"World War II: An American F-13 Superfortress made the first flight by an Allied aircraft over Tokyo since the Doolittle Raid in April 1942.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1941,"text":"American photographer Ansel Adams (pictured) shot Moonrise, Hernandez, New Mexico, one of his most famous photographs.","url":"https://en.wikipedia.org/wiki/Ansel_Adams"},{"year":1921,"text":"Frances Kyle was called to the Bar of Ireland, becoming the first female barrister in Ireland or Great Britain.","url":"https://en.wikipedia.org/wiki/Frances_Kyle"},{"year":1914,"text":"World War I: The first contingent of the First Australian Imperial Force departed Albany, Western Australia.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1824,"text":"The disposable ship Columbus (pictured) arrived in the The Downs off England, becoming, at that time, the largest vessel to have crossed the Atlantic Ocean.","url":"https://en.wikipedia.org/wiki/Disposable_ship"},{"year":1214,"text":"Byzantine–Seljuk wars: Seljuq Turks captured the important port city of Sinope.","url":"https://en.wikipedia.org/wiki/Byzantine%E2%80%93Seljuk_wars"},{"year":1141,"text":"After Empress Matilda released her rival King Stephen, he in turn released Robert of Gloucester, her strongest supporter, thus prolonging the Anglo-Norman civil war known as The Anarchy.","url":"https://en.wikipedia.org/wiki/Empress_Matilda"}],"births":[{"year":2003,"text":"Ernest Nuamah, Ghanaian footballer","url":"https://en.wikipedia.org/wiki/Ernest_Nuamah"},{"year":2003,"text":"Lautaro Rivero, Argentine footballer","url":"https://en.wikipedia.org/wiki/Lautaro_Rivero"},{"year":2002,"text":"NLE Choppa, American rapper and YouTuber","url":"https://en.wikipedia.org/wiki/NLE_Choppa"},{"year":2001,"text":"Alofiana Khan-Pereira, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Alofiana_Khan-Pereira"},{"year":2000,"text":"Gonzalo Plata, Ecuadorian footballer","url":"https://en.wikipedia.org/wiki/Gonzalo_Plata"},{"year":1998,"text":"Stuart Skinner, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Stuart_Skinner"},{"year":1997,"text":"Max Burkholder, American actor","url":"https://en.wikipedia.org/wiki/Max_Burkholder"},{"year":1997,"text":"Nordi Mukiele, French footballer","url":"https://en.wikipedia.org/wiki/Nordi_Mukiele"}],"deaths":[{"year":2025,"text":"Martha Layne Collins, American politician, 56th Governor of Kentucky (born 1936)","url":"https://en.wikipedia.org/wiki/Martha_Layne_Collins"},{"year":2025,"text":"John Farragher, Australian rugby league player (born 1957)","url":"https://en.wikipedia.org/wiki/John_Farragher"},{"year":2025,"text":"Carlos Manzo, Mexican politician (born 1985)","url":"https://en.wikipedia.org/wiki/Carlos_Manzo"},{"year":2023,"text":"Brian Brain, English cricketer (born 1940)","url":"https://en.wikipedia.org/wiki/Brian_Brain"},{"year":2022,"text":"Takeoff, member of the American hip-hop group Migos (born 1994)","url":"https://en.wikipedia.org/wiki/Takeoff_(rapper)"},{"year":2021,"text":"Hugo Dittfach, Canadian horse jockey (born 1936)","url":"https://en.wikipedia.org/wiki/Hugo_Dittfach"},{"year":2020,"text":"Keith Hitchins, American historian expert on Romanian history (born 1931)","url":"https://en.wikipedia.org/wiki/Keith_Hitchins"},{"year":2020,"text":"Lady Elizabeth Shakerley, British party planner, writer and socialite (born 1941)","url":"https://en.wikipedia.org/wiki/Lady_Elizabeth_Shakerley"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC 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Every item links to its source."}