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KI-Update kompakt: OpenAI, Fahrassistenzsystem, Desinformation, Optimus Bot 2.5
Das "KI-Update" liefert werktäglich eine Zusammenfassung der wichtigsten KI-Entwicklungen.
https://www.…

That’s amazing, I’ve got the same combination on my luggage!
https://cybersecuritynews.com/mcdonalds-ai-hiring-bot-leaks/

McDonald’s AI Hiring Bot With Password ‘123456’ Leaks Millions of Job-Seekers Data
A security vulnerability in McDonald's AI-powered hiring system has exposed the personal information of potentially 64 million job applicants.

Replaced article(s) found for math.AP. https://arxiv.org/list/math.AP/new
[1/1]:
- Strong relaxation limit and uniform time asymptotics of the Jin-Xin model in the $L^{p}$ framework
Timoth\'ee Crin-Barat, Ling-Yun Shou, Jianzhong Zhang
https://arxiv.org/abs/2311.04105 https://mastoxiv.page/@arXiv_mathAP_bot/111373086323167893
- Long time regularity for 3d gravity waves with vorticity
Daniel Ginsberg, Fabio Pusateri
https://arxiv.org/abs/2401.10096 https://mastoxiv.page/@arXiv_mathAP_bot/111780770295022186
- Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantil...
Richard Duong, Viktor Stein, Robert Beinert, Johannes Hertrich, Gabriele Steidl
https://arxiv.org/abs/2408.07498 https://mastoxiv.page/@arXiv_mathAP_bot/112964949403866709
- Regularity of solutions for degenerate or singular fully nonlinear integro-differential equations
Jiangwen Wang, Feida Jiang
https://arxiv.org/abs/2408.14779 https://mastoxiv.page/@arXiv_mathAP_bot/113038547448492855
- An overview of the stability of Sobolev inequalities on Riemannian manifolds with Ricci lower bounds
Francesco Nobili
https://arxiv.org/abs/2412.05935 https://mastoxiv.page/@arXiv_mathAP_bot/113627373212995916
- The $\mathcal{M}$-Operator and Uniqueness of Nonlinear Kinetic Equations
Ricardo Alonso, Maria Pia Gualdani, Weiran Sun
https://arxiv.org/abs/2506.20775 https://mastoxiv.page/@arXiv_mathAP_bot/114754297465809405
- Magnetic Stabilization of Compressible Flows: Global Existence in 3D Inviscid Non-Isentropic MHD ...
Jiahong Wu, Fuyi Xu, Xiaoping Zhai
https://arxiv.org/abs/2507.00888 https://mastoxiv.page/@arXiv_mathAP_bot/114782973460202498
- Free boundary regularity for a tumor growth model with obstacle
Giulia Bevilacqua, Matteo Carducci
https://arxiv.org/abs/2507.02837 https://mastoxiv.page/@arXiv_mathAP_bot/114794384026764859
- Three bound states with prescribed angular momentum to the cubic-quintic NLS equations in $\mathb...
Shuai Yao, Juntao Sun
https://arxiv.org/abs/2507.04057 https://mastoxiv.page/@arXiv_mathAP_bot/114817504459971991
- Some remarks on a mathematical model for water flow in porous media with competition between tran...
Judita Runczikov\'a, Jan Chleboun, Chiara Gavioli, Pavel Krej\v{c}\'i
https://arxiv.org/abs/2405.10751 https://mastoxiv.page/@arXiv_mathNA_bot/112472226685837805
- SDEs with critical general distributional drifts: sharp solvability and blow ups
D. Kinzebulatov, R. Vafadar
https://arxiv.org/abs/2506.09244 https://mastoxiv.page/@arXiv_mathPR_bot/114669448852836191
- One sided orthogonal polynomials and a pointwise convergence result for $SU(2)$-valued nonlinear ...
Michel Alexis, Gevorg Mnatsakanyan, Christoph Thiele
https://arxiv.org/abs/2507.05124 https://mastoxiv.page/@arXiv_mathCA_bot/114817025355217864
toXiv_bot_toot

Albania appoints an AI bot named Diella as the minister responsible for managing and awarding all public procurement tenders to combat corruption (Reuters)
https://www.reuters.com/technology/albania-appoints-ai-bot-minister-tackle-corruption-…

[2025-09-11 Thu (UTC), 59 new articles found for cs.CV Computer Vision and Pattern Recognition]
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[2025-08-11 Mon (UTC), 28 new articles found for cs.AI Artificial Intelligence]
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[2025-09-10 Wed (UTC), 1 new article found for cs.LO Logic in Computer Science]
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[2025-09-11 Thu (UTC), 33 new articles found for cs.CL Computation and Language]
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[2025-09-10 Wed (UTC), 3 new articles found for math.CT Category Theory]
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[2025-08-11 Mon (UTC), 1 new article found for cs.PL Programming Languages]
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[2025-09-10 Wed (UTC), 21 new articles found for cs.CR Cryptography and Security]
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[2025-09-10 Wed (UTC), 26 new articles found for cs.RO Robotics]
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[2025-09-10 Wed (UTC), 8 new articles found for cs.SE Software Engineering]
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[2025-09-10 Wed (UTC), 5 new articles found for stat.ML Machine Learning]
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[2025-08-11 Mon (UTC), 15 new articles found for cs.HC Human-Computer Interaction]
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[2025-09-10 Wed (UTC), 7 new articles found for cs.CY Computers and Society]
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[2025-08-11 Mon (UTC), 16 new articles found for math.CO Combinatorics]
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[2025-09-10 Wed (UTC), 1 new article found for math.LO Logic]
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Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Yuxing Liu, Yuze Ge, Rui Pan, An Kang, Tong Zhang
https://arxiv.org/abs/2509.07972 https://

Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Learning rate warmup is a popular and practical technique in training large-scale deep neural networks. Despite the huge success in practice, the theoretical advantages of this strategy of gradually increasing the learning rate at the beginning of the training process have not been fully understood. To resolve this gap between theory and practice, we first propose a novel family of generalized smoothness assumptions, and validate its applicability both theoretically and empirically. Under the n…

[2025-09-11 Thu (UTC), 13 new articles found for cs.AI Artificial Intelligence]
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[2025-07-10 Thu (UTC), 84 new articles found for cs.CV Computer Vision and Pattern Recognition]
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[2025-09-11 Thu (UTC), 5 new articles found for cs.LO Logic in Computer Science]
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[2025-08-11 Mon (UTC), no new articles found for math.CT Category Theory]
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[2025-09-11 Thu (UTC), 1 new article found for cs.PL Programming Languages]
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[2025-07-11 Fri (UTC), 23 new articles found for cs.RO Robotics]
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[2025-08-11 Mon (UTC), 27 new articles found for cs.CR Cryptography and Security]
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[2025-09-11 Thu (UTC), 8 new articles found for cs.SE Software Engineering]
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[2025-09-11 Thu (UTC), 4 new articles found for stat.ML Machine Learning]
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[2025-09-11 Thu (UTC), 15 new articles found for cs.HC Human-Computer Interaction]
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[2025-08-11 Mon (UTC), 15 new articles found for cs.CY Computers and Society]
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[2025-09-11 Thu (UTC), 7 new articles found for math.CO Combinatorics]
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[2025-09-11 Thu (UTC), 1 new article found for math.LO Logic]
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Customizing the Inductive Biases of Softmax Attention using Structured Matrices
Yilun Kuang, Noah Amsel, Sanae Lotfi, Shikai Qiu, Andres Potapczynski, Andrew Gordon Wilson
https://arxiv.org/abs/2509.07963

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair. While the low-dimensional projection improves efficiency, it causes information loss for certain tasks that have intrinsically high-dimensional inputs. Additionally, attention uses the same scoring function for all input pairs, without imposing a distance-dependent compute bias for neighboring tokens in the sequence. In this work, we …

[2025-07-11 Fri (UTC), 26 new articles found for cs.AI Artificial Intelligence]
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[2025-08-11 Mon (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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[2025-07-11 Fri (UTC), 3 new articles found for math.CT Category Theory]
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[2025-09-10 Wed (UTC), 3 new articles found for cs.PL Programming Languages]
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[2025-08-11 Mon (UTC), 25 new articles found for cs.RO Robotics]
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[2025-08-11 Mon (UTC), 12 new articles found for cs.SE Software Engineering]
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[2025-07-11 Fri (UTC), 18 new articles found for cs.CR Cryptography and Security]
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[2025-08-11 Mon (UTC), 6 new articles found for stat.ML Machine Learning]
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[2025-09-10 Wed (UTC), 15 new articles found for cs.HC Human-Computer Interaction]
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[2025-07-11 Fri (UTC), 7 new articles found for cs.CY Computers and Society]
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[2025-09-10 Wed (UTC), 18 new articles found for math.CO Combinatorics]
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[2025-07-10 Thu (UTC), 2 new articles found for math.LO Logic]
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ACE and Diverse Generalization via Selective Disagreement
Oliver Daniels, Stuart Armstrong, Alexandre Maranh\~ao, Mahirah Fairuz Rahman, Benjamin M. Marlin, Rebecca Gorman
https://arxiv.org/abs/2509.07955

ACE and Diverse Generalization via Selective Disagreement
Deep neural networks are notoriously sensitive to spurious correlations - where a model learns a shortcut that fails out-of-distribution. Existing work on spurious correlations has often focused on incomplete correlations,leveraging access to labeled instances that break the correlation. But in cases where the spurious correlations are complete, the correct generalization is fundamentally \textit{underspecified}. To resolve this underspecification, we propose learning a set of concepts that are…

[2025-07-10 Thu (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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[2025-07-10 Thu (UTC), no new articles found for math.CT Category Theory]
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[2025-07-11 Fri (UTC), 1 new article found for cs.PL Programming Languages]
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[2025-07-10 Thu (UTC), 29 new articles found for cs.CR Cryptography and Security]
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[2025-07-10 Thu (UTC), 9 new articles found for cs.SE Software Engineering]
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[2025-07-10 Thu (UTC), 7 new articles found for stat.ML Machine Learning]
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[2025-07-11 Fri (UTC), 6 new articles found for cs.HC Human-Computer Interaction]
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[2025-07-10 Thu (UTC), 13 new articles found for cs.CY Computers and Society]
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[2025-08-11 Mon (UTC), 7 new articles found for math.LO Logic]
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Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

[2025-07-11 Fri (UTC), no new articles found for cs.LO Logic in Computer Science]
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[2025-07-10 Thu (UTC), 4 new articles found for cs.PL Programming Languages]
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[2025-07-11 Fri (UTC), 6 new articles found for stat.ML Machine Learning]
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[2025-07-10 Thu (UTC), 10 new articles found for cs.HC Human-Computer Interaction]
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One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
Yuan Pu, Yazhe Niu, Jia Tang, Junyu Xiong, Shuai Hu, Hongsheng Li
https://arxiv.org/abs/2509.07945

One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
In heterogeneous multi-task learning, tasks not only exhibit diverse observation and action spaces but also vary substantially in intrinsic difficulty. While conventional multi-task world models like UniZero excel in single-task settings, we find that when handling large-scale heterogeneous environments, gradient conflicts and the loss of model plasticity often constrain their sample and computational efficiency. In this work, we address these challenges from two perspectives: the single learni…

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
https://

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can …

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
https://arxiv.org/abs/2509.07905 https://

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tas…

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
Qing-xin Meng, Xia Lei, Jian-wei Liu
https://arxiv.org/abs/2509.07901 https://arx…

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
This paper investigates the problem of Online Convex-Concave Optimization, which extends Online Convex Optimization to two-player time-varying convex-concave games. The goal is to minimize the dynamic duality gap (D-DGap), a critical performance measure that evaluates players' strategies against arbitrary comparator sequences. Existing algorithms fail to deliver optimal performance, particularly in stationary or predictable environments. To address this, we propose a novel modular algorithm wit…

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Philipp Lepold, Jonas Leichtle, Tobias R\"oddiger, Michael Beigl
https://arxiv.org/abs/2509.07896

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG…

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Katherine Berry, Liang Cheng
https://arxiv.org/abs/2509.07887 https://

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug inter…

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Yajun Yu, Steve Jiang, Robert Timmerman, Hao Peng
https://arxiv.org/abs/2509.07872

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (MRI images) and do…

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin, Christopher Tosh, Wesley Tansey
https://arxiv.org/abs/2509.07850 https://

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Personalizing combination therapies in oncology requires navigating an immense space of possible drug and dose combinations, a task that remains largely infeasible through exhaustive experimentation. Recent developments in patient-derived models have enabled high-throughput ex vivo screening, but the number of feasible experiments is limited. Further, a tight therapeutic window makes gathering molecular profiling information (e.g. RNA-seq) impractical as a means of guiding drug response predict…

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Mohammad Zana Majidi, Sajjad Karimi, Teng Wang, Robert Kluger, Reginald Souleyrette
https://arxiv.org/abs/2509.07845

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed t…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
Songkai Ma, Zhaorui Zhang, Sheng Di, Benben Liu, Xiaodong Yu, Xiaoyi Lu, Dan Wang
https://arxiv.org/abs/2509.07727

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has emerged as a significant challenge. Offloading the non-activated experts to main memory has been identified as an efficient approach to address such a problem, while it brings the challenges of transferring the expert between the GPU memory and main memory. We need to explore an efficient approach to compress the exper…

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Shusen Ma, Tianhao Zhang, Qijiu Xia, Yun-Bo Zhao
https://arxiv.org/abs/2509.07725

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses variable missing using attention-based imputation and adaptive graph learning for the first time, it lacks interpretability and fails to capture more latent temporal patterns due to its simple recursive units (RUs). To overcome these limitations, we propose the Interpretable Bidi…

FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings
Pierre Lambert, Edouard Couplet, Michel Verleysen, John Aldo Lee
https://arxiv.org/abs/2509.07681

FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings
Neighbour embeddings (NE) allow the representation of high dimensional datasets into lower dimensional spaces and are often used in data visualisation. In practice, accelerated approximations are employed to handle very large datasets. Accelerating NE is challenging, and two main directions have been explored: very coarse approximations based on negative sampling (as in UMAP) achieve high effective speed but may lack quality in the extracted structures; less coarse approximations, as used in FI…

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Lachlan Simpson, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew
https://arxiv.org/abs/2509.07648

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on t…

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Ali Nawaz, Amir Ahmad, Shehroz S. Khan
https://arxiv.org/abs/2509.07605 …

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare. While numerous studies have explored rebalancing techniques to address this issue, less attention has been given to evaluating the performance of binary classifiers under imbalance when no such techniques are applied. Therefore, the goal of this study is to assess the performance of binary classifiers…

K2-Think: A Parameter-Efficient Reasoning System
Zhoujun Cheng, Richard Fan, Shibo Hao, Taylor W. Killian, Haonan Li, Suqi Sun, Hector Ren, Alexander Moreno, Daqian Zhang, Tianjun Zhong, Yuxin Xiong, Yuanzhe Hu, Yutao Xie, Xudong Han, Yuqi Wang, Varad Pimpalkhute, Yonghao Zhuang, Aaryamonvikram Singh, Xuezhi Liang, Anze Xie, Jianshu She, Desai Fan, Chengqian Gao, Liqun Ma, Mikhail Yurochkin, John Maggs, Xuezhe Ma, Guowei He, Zhiting Hu, Zhengzhong Liu, Eric P. Xing

K2-Think: A Parameter-Efficient Reasoning System
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewa…

Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
Mehdi Bejani, Marco Mauri, Daniele Acconcia, Simone Todaro, Stefano Mariani
https://arxiv.org/abs/2509.07603

Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
This paper presents an innovative Transformer-based deep learning strategy for optimizing the placement of sensors aiming at structural health monitoring of semiconductor probe cards. Failures in probe cards, including substrate cracks and loosened screws, would critically affect semiconductor manufacturing yield and reliability. Some failure modes could be detected by equipping a probe card with adequate sensors. Frequency response functions from simulated failure scenarios are adopted within …

Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
Liya Gaynutdinova, Martin Do\v{s}k\'a\v{r}, Ond\v{r}ej Roko\v{s}, Ivana Pultarov\'a
https://arxiv.org/abs/2509.07579

Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs) relevant to multiscale modeling, but they often fail when applied to materials with discontinuous coefficients, such as media with piecewise constant properties. This paper introduces a dual formulation for the PINN framework to improve the reliability of the homogenization of periodic thermo-conductive composites, for both strong and variational (weak) formulations. The dual approach fa…

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
Zakeria Sharif Ali
https://arxiv.org/abs/2509.07569 https://arxiv.org/pdf/2509.07569

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
This paper introduces the Univariate Gaussian Mixture Model Neural Network (uGMM-NN), a novel neural architecture that embeds probabilistic reasoning directly into the computational units of deep networks. Unlike traditional neurons, which apply weighted sums followed by fixed nonlinearities, each uGMM-NN node parameterizes its activations as a univariate Gaussian mixture, with learnable means, variances, and mixing coefficients. This design enables richer representations by capturing multimoda…

$\Delta L$ Normalization: Rethink Loss Aggregation in RLVR
Zhiyuan He, Xufang Luo, Yike Zhang, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2509.07558 https://

$ΔL$ Normalization: Rethink Loss Aggregation in RLVR
We propose $ΔL$ Normalization, a simple yet effective loss aggregation method tailored to the characteristic of dynamic generation lengths in Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has demonstrated strong potential in improving the reasoning capabilities of large language models (LLMs), but a major challenge lies in the large variability of response lengths during training, which leads to high gradient variance and unstable optimization. Although previous methods…

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Jad Yehya, Mansour Benbakoura, C\'edric Allain, Beno\^it Malezieux, Matthieu Kowalski, Thomas Moreau
https://arxiv.org/abs/2509.07523

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Identifying recurring patterns and rare events in large-scale signals is a fundamental challenge in fields such as astronomy, physical simulations, and biomedical science. Convolutional Dictionary Learning (CDL) offers a powerful framework for modeling local structures in signals, but its use for detecting rare or anomalous events remains largely unexplored. In particular, CDL faces two key challenges in this setting: high computational cost and sensitivity to artifacts and outliers. In this pa…

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Tom\'as Arias-Vergara, Andreas Maier, Siming Bayer
https://arxiv.org/abs/2509.07515

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate change, securing water resources and supply has emerged as an urgent global issue with extensive socioeconomic ramifications. Hourly consumption data from end-users have yielded substantial insights for projecting demand across regions characterized by diverse consumption patterns. Nevertheless, the prediction of water…

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
Antoine Ledent, Petr Kasalick\'y, Rodrigo Alves, Hady W. Lauw
https://arxiv.org/abs/2509.07499

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
We introduce a new convolutional AutoEncoder architecture for user modelling and recommendation tasks with several improvements over the state of the art. Firstly, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Secondly, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as `implicit feedback')…

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
Long Li, Jiaran Hao, Jason Klein Liu, Zhijian Zhou, Xiaoyu Tan, Wei Chu, Zhe Wang, Shirui Pan, Chao Qu, Yuan Qi
https://arxiv.org/abs/2509.07430

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pass@k) despite improvements in single-attempt accuracy (Pass@1). This is often accompanied by catastrophic forgetting, where models lose previously acquired skills. While various methods have been proposed, the choice and function of the divergence term have been surprisingly unexamined as a proactive solution. We argu…

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
Arslan Majal, Aamir Hussain Chughtai, Muhammad Tahir
https://arxiv.org/abs/2509.07415 http…

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods …

Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Soyoun Kim, Sunyoung Moon, Sua Lee, Jaeyoung Choi, Hyemin Lee, Sangmi Chai
https://arxiv.org/abs/2509.07392

Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines.

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Qasim Zia, Avais Jan, Zafar Iqbal, Muhammad Mumtaz Ali, Mukarram Ali, Murray Patterson
https://arxiv.org/abs/2509.07388

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized card…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Qihu Xie, Yuan Li, Yi Kang
https://arxiv.org/abs/2509.07373 https…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Implicit neural representations have recently been extended to represent convolutional neural network weights via neural representation for neural networks, offering promising parameter compression benefits. However, standard multi-layer perceptrons used in neural representation for neural networks exhibit a pronounced spectral bias, hampering their ability to reconstruct high-frequency details effectively. In this paper, we propose SBS, a parameter-efficient enhancement to neural representatio…

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu
https://arxiv.org/abs/2509.07342

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a tempor…

General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases
Li-Chin Chen, Ji-Tian Sheu, Yuh-Jue Chuang
https://arxiv.org/abs/2509.07330 https://…

General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases
Demographic attributes are universally present in electronic health records and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often relegated to auxiliary roles in model design, with limited attention has been given to learning their representations. This study proposes a General Demographic Pre-trained (GDP) model as a foundational representation framework tailored to age and gender. The model is pre-trained …

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
Alyssa Unell, Noel C. F. Codella, Sam Preston, Peniel Argaw, Wen-wai Yim, Zelalem Gero, Cliff Wong, Rajesh Jena, Eric Horvitz, Amanda K. Hall, Ruican Rachel Zhong, Jiachen Li, Shrey Jain, Mu Wei, Matthew Lungren, Hoifung Poon
https://arxiv.org/abs/2509.07325…

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatment recommendations is time-intensive, requires specialized expertise, and is prone to error. Advances in large language model (LLM) capabilities promise to reduce the time required to generate treatment recommendations and improve accuracy. We present an LLM agent-based approach to automatically generate guideline-con…

ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
Jeff Shen, Lindsay Smith
https://arxiv.org/abs/2509.07282 https://ar…

ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
We present cryptogram solving as an ideal testbed for studying neural network generalization in combinatorially complex domains. In this task, models must decrypt text encoded with substitution ciphers, choosing from 26! possible mappings without explicit access to the cipher. We develop ALICE (an Architecture for Learning Interpretable Cryptogram dEcipherment): a simple encoder-only Transformer that sets a new state-of-the-art for both accuracy and speed on this decryption problem. Surprisingl…

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell, Minh Nguyen, Chandrajit Bajaj
https://arxiv.org/abs/2509.07280

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesian inference. Although conservative, dissipative, and port-Hamiltonian systems might share the same initial total energy of a closed system, it is challenging for a single Hamiltonian network model to capture the distinctive and varying motion dynamics and physics of a phase space, from sampled observational phase spac…

GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning
Evgeny Alves Limarenko, Anastasiia Alexandrovna Studenikina
https://arxiv.org/abs/2509.07252

GCond: Gradient Conflict Resolution via Accumulation-based Stabilization for Large-Scale Multi-Task Learning
In multi-task learning (MTL), gradient conflict poses a significant challenge. Effective methods for addressing this problem, including PCGrad, CAGrad, and GradNorm, in their original implementations are computationally demanding, which significantly limits their application in modern large models and transformers. We propose Gradient Conductor (GCond), a method that builds upon PCGrad principles by combining them with gradient accumulation and an adaptive arbitration mechanism. We evaluated GC…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Shalev Manor, Mohammad Kohandel
https://arxiv.org/abs/2509.07245 https://arxiv.org/pdf/2…

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
Solving inverse problems with Physics-Informed Neural Networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present Inverse-Parameter Basis PINNs (IP-Basis PINNs), a meta-learning framework that extends the foundational work of Desai et al. (2022) to enable rapid and efficient inference for inverse problems. Our method employs an offline-online decomposition: a deep network is first trained offl…

Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning
Pranav Pawar, Dhwaj Jain, Varun Gupta, Kaustav Dedhia, Dashrath Kale, Sudhir Dhekane
https://arxiv.org/abs/2509.07238

Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning
This paper presents a practical investigation into fine-tuning model parameters for mathematical reasoning tasks through experimenting with various configurations including randomness control, reasoning depth, and sampling strategies, careful tuning demonstrates substantial improvements in efficiency as well as performance. A holistically optimized framework is introduced for five state-of-the-art models on mathematical reasoning tasks, exhibiting significant performance boosts while maintainin…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Interleaving Reasoning for Better Text-to-Image Generation
Huang, Chen, Xie, Cao, Tang, Shen, Yin, Hu, Wang, Tang, Qiao, Guo, Hu, Yin, Torr, Cheng, Ouyang, Lin

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
Yu Zhang, Yuqi Xie, Huihan Liu, Rutav Shah, Michael Wan, Linxi Fan, Yuke Zhu

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
S\'egol\`ene Martin, Anne Gagneux, Paul Hagemann, Gabriele Steidl

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraints
Xiaodong Cui, A F M Saif, Brian Kingsbury, Tianyi Chen