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","authors":["Andrew Kiruluta","Priscilla Burity"],"url":"http://arxiv.org/abs/2511.10655v1","arxiv_id":"2511.10655v1","published":"2025-11-02T21:57:15Z"},{"title":"Continual Learning, Not Training: Online Adaptation For Agents","abstract":"Continual Learning (CL) methods have traditionally focused on mitigating catastrophic forgetting through gradient-based retraining, an approach ill-suited for deployed agents that must adapt in real time. We introduce our Adaptive Teaching and Learning System (ATLAS), a dual-agent architecture that decouples reasoning (Teacher) from execution (Student) and incorporates a persistent learning memory that stores distill","authors":["Aman Jaglan","Jarrod Barnes"],"url":"http://arxiv.org/abs/2511.01093v1","arxiv_id":"2511.01093v1","published":"2025-11-02T21:48:31Z"},{"title":"SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices","abstract":"The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance In","authors":["Md. Abid Hasan Rafi","Mst. Fatematuj Johora","Pankaj Bhowmik"],"url":"http://arxiv.org/abs/2511.01087v1","arxiv_id":"2511.01087v1","published":"2025-11-02T21:37:38Z"},{"title":"GeoToken: Hierarchical Geolocalization of Images via Next Token Prediction","abstract":"Image geolocalization, the task of determining an image's geographic origin, poses significant challenges, largely due to visual similarities across disparate locations and the large search space. To address these issues, we propose a hierarchical sequence prediction approach inspired by how humans narrow down locations from broad regions to specific addresses. Analogously, our model predicts geographic tokens hierar","authors":["Narges Ghasemi","Amir Ziashahabi","Salman Avestimehr","Cyrus Shahabi"],"url":"http://arxiv.org/abs/2511.01082v1","arxiv_id":"2511.01082v1","published":"2025-11-02T21:30:06Z"},{"title":"Deep one-gate per layer networks with skip connections are universal classifiers","abstract":"This paper shows how a multilayer perceptron with two hidden layers, which has been designed to classify two classes of data points, can easily be transformed into a deep neural network with one-gate layers and skip connections.","authors":["Raul Rojas"],"url":"http://arxiv.org/abs/2511.05552v1","arxiv_id":"2511.05552v1","published":"2025-11-02T21:12:38Z"},{"title":"Empirical Characterization of Temporal Constraint Processing in LLMs","abstract":"When deploying LLMs in agentic architectures requiring real-time decisions under temporal constraints, we assume they reliably determine whether action windows remain open or have closed. This assumption is untested. We characterize temporal constraint processing across eight production-scale models (2.8-8B parameters) using deadline detection tasks, revealing systematic deployment risks: bimodal performance distribu","authors":["Javier Marín"],"url":"http://arxiv.org/abs/2511.10654v1","arxiv_id":"2511.10654v1","published":"2025-11-02T20:03:52Z"},{"title":"Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Evaluation of Forward-Only Algorithms","abstract":"The long-held assumption that backpropagation (BP) is essential for state-of-the-art performance is challenged by this work. We present rigorous, hardware-validated evidence that the Mono-Forward (MF) algorithm, a backpropagation-free method, consistently surpasses an optimally tuned BP baseline in classification accuracy on its native Multi-Layer Perceptron (MLP) architectures. This superior generalization is achiev","authors":["Przemysław Spyra","Witold Dzwinel"],"url":"http://arxiv.org/abs/2511.01061v1","arxiv_id":"2511.01061v1","published":"2025-11-02T19:48:44Z"},{"title":"Efficient Test-Time Retrieval Augmented Generation","abstract":"Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but la","authors":["Hailong Yin","Bin Zhu","Jingjing Chen","Chong-Wah Ngo"],"url":"http://arxiv.org/abs/2511.01059v1","arxiv_id":"2511.01059v1","published":"2025-11-02T19:32:39Z"},{"title":"Knowledge Elicitation with Large Language Models for Interpretable Cancer Stage Identification from Pathology Reports","abstract":"Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports poses a persistent challenge. Existing natural language processing (NLP) and machine learning (ML) strategies often depend on large annotated datasets, limiting their scalability and adaptability. In this study, we introduce two Knowledge Elicitation methods designed to ov","authors":["Yeawon Lee","Christopher C. Yang","Chia-Hsuan Chang","Grace Lu-Yao"],"url":"http://arxiv.org/abs/2511.01052v1","arxiv_id":"2511.01052v1","published":"2025-11-02T19:00:40Z"},{"title":"HAFixAgent: History-Aware Program Repair Agent","abstract":"Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic","authors":["Yu Shi","Hao Li","Bram Adams","Ahmed E. Hassan"],"url":"http://arxiv.org/abs/2511.01047v3","arxiv_id":"2511.01047v3","published":"2025-11-02T18:45:34Z"},{"title":"The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold","abstract":"Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete memorization of the training data. Previous research has linked this delayed generalization to representation learning driven by weight decay, but the precise underlying dynamics remain elusive. In this paper, we argue that post-memorization learning can be understood through the","authors":["Tiberiu Musat"],"url":"http://arxiv.org/abs/2511.01938v3","arxiv_id":"2511.01938v3","published":"2025-11-02T18:44:42Z"},{"title":"On the Emergence of Induction Heads for In-Context Learning","abstract":"Transformers have become the dominant architecture for natural language processing. Part of their success is owed to a remarkable capability known as in-context learning (ICL): they can acquire and apply novel associations solely from their input context, without any updates to their weights. In this work, we study the emergence of induction heads, a previously identified mechanism in two-layer transformers that is p","authors":["Tiberiu Musat","Tiago Pimentel","Lorenzo Noci","Alessandro Stolfo","Mrinmaya Sachan","Thomas Hofmann"],"url":"http://arxiv.org/abs/2511.01033v2","arxiv_id":"2511.01033v2","published":"2025-11-02T18:12:06Z"},{"title":"Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfer","abstract":"We analyze subliminal transfer in Transformer models, where a teacher embeds hidden traits that can be linearly decoded by a student without degrading main-task performance. Prior work often attributes transferability to global representational similarity, typically quantified with Centered Kernel Alignment (CKA). Using synthetic corpora with disentangled public and private labels, we distill students under matched a","authors":["Ayşe Selin Okatan","Mustafa İlhan Akbaş","Laxima Niure Kandel","Berker Peköz"],"url":"http://arxiv.org/abs/2511.01023v1","arxiv_id":"2511.01023v1","published":"2025-11-02T17:34:43Z"},{"title":"Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR","abstract":"Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (RLVR) pipelines filter out ``easy'' problems for training efficiency, leaving the model to train primarily on harder problems that require longer reasoning chains. This skews the output length distribution upward, resulting in a \\textbf{mode","authors":["Abdelaziz Bounhar","Hadi Abdine","Evan Dufraisse","Ahmad Chamma","Amr Mohamed","Dani Bouch"],"url":"http://arxiv.org/abs/2511.01937v2","arxiv_id":"2511.01937v2","published":"2025-11-02T17:29:16Z"},{"title":"OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights","abstract":"Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified \"hallucinations\" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmo","authors":["Bowen Chen","Jayesh Gajbhar","Gregory Dusek","Rob Redmon","Patrick Hogan","Paul Liu"],"url":"http://arxiv.org/abs/2511.01019v2","arxiv_id":"2511.01019v2","published":"2025-11-02T17:23:58Z"},{"title":"AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS)","abstract":"Recurrent exacerbations remain a common yet preventable outcome for many children with asthma. Machine learning (ML) algorithms using electronic medical records (EMR) could allow accurate identification of children at risk for exacerbations and facilitate referral for preventative comprehensive care to avoid this morbidity. We developed ML algorithms to predict repeat severe exacerbations (i.e. asthma-related emergen","authors":["Hui-Lee Ooi","Nicholas Mitsakakis","Margerie Huet Dastarac","Roger Zemek","Amy C. Plint","Jeff Gilchrist"],"url":"http://arxiv.org/abs/2511.01018v1","arxiv_id":"2511.01018v1","published":"2025-11-02T17:22:44Z"},{"title":"Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies","abstract":"The determination of sample size in qualitative research has traditionally relied on the subjective and often ambiguous principle of data saturation, which can lead to inconsistencies and threaten methodological rigor. This study introduces a new, systematic model based on machine learning (ML) to make this process more objective. Utilizing a dataset derived from five fundamental qualitative research approaches - nam","authors":["Hasan Tutar","Caner Erden","Ümit Şentürk"],"url":"http://arxiv.org/abs/2511.01935v1","arxiv_id":"2511.01935v1","published":"2025-11-02T17:18:51Z"},{"title":"Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch","abstract":"Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often struggles to generalize effectively to unfamiliar or intricate tool-use scenarios. Recently, reinforcement learning (RL) paradigm can endow LLMs ","authors":["Yirong Zeng","Xiao Ding","Yutai Hou","Yuxian Wang","Li Du","Juyi Dai"],"url":"http://arxiv.org/abs/2511.01934v2","arxiv_id":"2511.01934v2","published":"2025-11-02T16:33:45Z"},{"title":"Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models","abstract":"Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be provided via visual features in generated images, but is difficult for human users to understand. Explainability in natural language is a better choice, but the existing a","authors":["Haoming Wang","Wei Gao"],"url":"http://arxiv.org/abs/2511.01932v1","arxiv_id":"2511.01932v1","published":"2025-11-02T16:08:24Z"},{"title":"Aligning LLM agents with human learning and adjustment behavior: a dual agent approach","abstract":"Effective modeling of how human travelers learn and adjust their travel behavior from interacting with transportation systems is critical for system assessment and planning. However, this task is also difficult due to the complex cognition and decision-making involved in such behavior. Recent research has begun to leverage Large Language Model (LLM) agents for this task. Building on this, we introduce a novel dual-ag","authors":["Tianming Liu","Jirong Yang","Yafeng Yin","Manzi Li","Linghao Wang","Zheng Zhu"],"url":"http://arxiv.org/abs/2511.00993v1","arxiv_id":"2511.00993v1","published":"2025-11-02T16:05:33Z"},{"title":"ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL","abstract":"Large Language Models (LLMs) have demonstrated remarkable progress in translating natural language to SQL, but a significant semantic gap persists between their general knowledge and domain-specific semantics of databases. Historical translation logs constitute a rich source of this missing in-domain knowledge, where SQL queries inherently encapsulate real-world usage patterns of database schema. Existing methods pri","authors":["Yiwen Jiao","Tonghui Ren","Yuche Gao","Zhenying He","Yinan Jing","Kai Zhang"],"url":"http://arxiv.org/abs/2511.00985v2","arxiv_id":"2511.00985v2","published":"2025-11-02T15:57:18Z"},{"title":"Keys in the Weights: Transformer Authentication Using Model-Bound Latent Representations","abstract":"We introduce Model-Bound Latent Exchange (MoBLE), a decoder-binding property in Transformer autoencoders formalized as Zero-Shot Decoder Non-Transferability (ZSDN). In identity tasks using iso-architectural models trained on identical data but differing in seeds, self-decoding achieves more than 0.91 exact match and 0.98 token accuracy, while zero-shot cross-decoding collapses to chance without exact matches. This se","authors":["Ayşe S. Okatan","Mustafa İlhan Akbaş","Laxima Niure Kandel","Berker Peköz"],"url":"http://arxiv.org/abs/2511.00973v1","arxiv_id":"2511.00973v1","published":"2025-11-02T15:29:44Z"},{"title":"Using Synthetic Data to estimate the True Error is theoretically and practically doable","abstract":"Accurately evaluating model performance is crucial for deploying machine learning systems in real-world applications. Traditional methods often require a sufficiently large labeled test set to ensure a reliable evaluation. However, in many contexts, a large labeled dataset is costly and labor-intensive. Therefore, we sometimes have to do evaluation by a few labeled samples, which is theoretically challenging. Recent ","authors":["Hai Hoang Thanh","Duy-Tung Nguyen","Hung The Tran","Khoat Than"],"url":"http://arxiv.org/abs/2511.00964v1","arxiv_id":"2511.00964v1","published":"2025-11-02T15:00:12Z"},{"title":"The Riddle of Reflection: Evaluating Reasoning and Self-Awareness in Multilingual LLMs using Indian Riddles","abstract":"The extent to which large language models (LLMs) can perform culturally grounded reasoning across non-English languages remains underexplored. This paper examines the reasoning and self-assessment abilities of LLMs across seven major Indian languages-Bengali, Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We introduce a multilingual riddle dataset combining traditional riddles with context-reconstructed vari","authors":["Abhinav P M","Ojasva Saxena","Oswald C","Parameswari Krishnamurthy"],"url":"http://arxiv.org/abs/2511.00960v2","arxiv_id":"2511.00960v2","published":"2025-11-02T14:40:36Z"},{"title":"The Hidden Power of Normalization Layers in Neural Networks: Exponential Capacity Control","abstract":"Normalization layers are critical components of modern AI systems, such as ChatGPT, Gemini, DeepSeek, etc. Empirically, they are known to stabilize training dynamics and improve generalization ability. However, the underlying theoretical mechanism by which normalization layers contribute to both optimization and generalization remains largely unexplained, especially when using many normalization layers in a deep neur","authors":["Khoat Than"],"url":"http://arxiv.org/abs/2511.00958v2","arxiv_id":"2511.00958v2","published":"2025-11-02T14:38:20Z"}],"history":{"events":[{"year":2008,"text":"Lewis Hamilton (pictured) overtook Timo Glock in the final corners of the Brazilian Grand Prix to win the Formula One championship by one point.","url":"https://en.wikipedia.org/wiki/Lewis_Hamilton"},{"year":2000,"text":"As members of Expedition 1, American astronaut William Shepherd and Russian cosmonauts Sergei Krikalev and Yuri Gidzenko (all pictured) became the first resident crew to arrive at the International Space Station.","url":"https://en.wikipedia.org/wiki/Expedition_1"},{"year":1997,"text":"Tropical Storm Linda made landfall in the Mekong Delta in Vietnam, causing more than 3,000 deaths.","url":"https://en.wikipedia.org/wiki/Tropical_Storm_Linda_(1997)"},{"year":1994,"text":"A lightning strike ruptured three oil tanks near Dronka, Egypt, causing a flood that killed 469 people.","url":"https://en.wikipedia.org/wiki/1994_Durunka_floods_and_lightning_strike"},{"year":1984,"text":"The serial killer Velma Barfield became the first woman to be executed in the United States since 1962.","url":"https://en.wikipedia.org/wiki/Velma_Barfield"},{"year":1963,"text":"President Ngô Đình Diệm of South Vietnam was assassinated, marking the culmination of a coup d'état led by Dương Văn Minh.","url":"https://en.wikipedia.org/wiki/Ngo_Dinh_Diem"},{"year":1957,"text":"A large number of people witnessed a fiery object in the sky near Levelland, Texas, which the United States Air Force said was ball lightning.","url":"https://en.wikipedia.org/wiki/Levelland_UFO_case"},{"year":1949,"text":"The Dutch–Indonesian Round Table Conference ended with the Netherlands agreeing to transfer sovereignty of the Dutch East Indies to the United States of Indonesia.","url":"https://en.wikipedia.org/wiki/Dutch%E2%80%93Indonesian_Round_Table_Conference"},{"year":1943,"text":"World War II: A U.S. Navy task force turned away an Imperial Japanese Navy formation at the Battle of Empress Augusta Bay, thus protecting the landings at Cape Torokina.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1932,"text":"The Australian military began a \"war against emus\", flightless native birds blamed for widespread damage to crops in Western Australia.","url":"https://en.wikipedia.org/wiki/Australian_Defence_Force"},{"year":1917,"text":"The British government issued the Balfour Declaration in support of a Jewish homeland in Palestine, then an Ottoman region with a small Jewish minority.","url":"https://en.wikipedia.org/wiki/Balfour_Declaration"},{"year":1880,"text":"James A. Garfield was elected as president of the United States; the election is the closest to date by popular vote margin.","url":"https://en.wikipedia.org/wiki/James_A._Garfield"},{"year":619,"text":"Emperor Gaozu of Tang allowed the assassination of a khagan of the Western Turkic Khaganate by Eastern Turkic rivals, one of the earliest events in the Tang campaigns against the Western Turks.","url":"https://en.wikipedia.org/wiki/Emperor_Gaozu_of_Tang"}],"births":[{"year":2001,"text":"Moisés Caicedo, Ecuadorian footballer","url":"https://en.wikipedia.org/wiki/Mois%C3%A9s_Caicedo"},{"year":1999,"text":"Park Woo-jin, South Korean singer","url":"https://en.wikipedia.org/wiki/Park_Woo-jin"},{"year":1998,"text":"Jordan Love, American football player","url":"https://en.wikipedia.org/wiki/Jordan_Love"},{"year":1997,"text":"Filip Hronek, Czech ice hockey player","url":"https://en.wikipedia.org/wiki/Filip_Hronek"},{"year":1997,"text":"Davis Keillor-Dunn, English footballer","url":"https://en.wikipedia.org/wiki/Davis_Keillor-Dunn"},{"year":1995,"text":"Hanna Öberg, Swedish biathlete","url":"https://en.wikipedia.org/wiki/Hanna_%C3%96berg"},{"year":1994,"text":"Shaq Coulthirst, English footballer","url":"https://en.wikipedia.org/wiki/Shaq_Coulthirst"},{"year":1991,"text":"Jimmy Garoppolo, American football player","url":"https://en.wikipedia.org/wiki/Jimmy_Garoppolo"}],"deaths":[{"year":2024,"text":"Janey Godley, Scottish actor, writer and comedian (born 1961)","url":"https://en.wikipedia.org/wiki/Janey_Godley"},{"year":2024,"text":"Alan Rachins, American actor (born 1942)","url":"https://en.wikipedia.org/wiki/Alan_Rachins"},{"year":2024,"text":"Paul Stephenson, British civil rights activist (born 1937)","url":"https://en.wikipedia.org/wiki/Paul_Stephenson_(civil_rights_campaigner)"},{"year":2023,"text":"Humaira Himu, Bangladeshi actress (born 1985)","url":"https://en.wikipedia.org/wiki/Humaira_Himu"},{"year":2022,"text":"Atilio Stampone, Argentine pianist and composer (born 1926)","url":"https://en.wikipedia.org/wiki/Atilio_Stampone"},{"year":2021,"text":"Neal Smith, American politician (born 1920)","url":"https://en.wikipedia.org/wiki/Neal_Smith_(politician)"},{"year":2019,"text":"Walter Mercado, Puerto Rican television personality, astrologer, actor, and dancer (born 1932)","url":"https://en.wikipedia.org/wiki/Walter_Mercado"},{"year":2018,"text":"Raymond Chow, Hong Kong film producer (born 1927)","url":"https://en.wikipedia.org/wiki/Raymond_Chow"}]},"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."}