20260103_005221/papers.csv

Rank,ID,Title,Authors,Year,Venue,Track,Status,Primary Area,Keywords,Citations,BM25 Score,Combined Score,DOI,URL,PDF,Source,TLDR,Abstract
1,xVw8YNEtH3,Reset Method based on the Theory of Manifold Optimization on Real Manifolds,Weiping Liu; Jiajun Wang; He Li; Youfa Liu; Jingui Zou,2025,ICLR 2025,main,Reject,optimization,Manifold Optimization;Real Manifolds;Method;Deep Learning.,0,15.862,0.000,,https://openreview.net/forum?id=xVw8YNEtH3,,offline_iclr,,"Manifold optimization is prominent in the fields of applied mathematics, statistics, machine learning, and in particular, deep learning. By leveraging the intrinsic geometric properties of manifolds, constrained optimization problems can be transformed into unconstrained optimization problems on cer"
2,WA35e2vPlFT,Neural Implicit Manifold Learning for Topology-Aware Generative Modelling,Brendan Leigh Ross; Gabriel Loaiza-Ganem; Anthony L. Caterini; Jesse C Cresswell,2023,ICLR 2023,main,Reject,,Manifold Learning;Unsupervised Learning;Density Estimation;Topology;Differential Geometry;Generative Modelling,0,15.828,0.000,,https://openreview.net/forum?id=WA35e2vPlFT,,offline_iclr,We propose a new model for probability distributions on topologically complex data manifolds which learns manifolds implicitly as the set of zeros of a neural network and then learns the distribution within using a constrained energy-based model.,"Natural data observed in $\mathbb{R}^n$ is often constrained to an $m$-dimensional manifold $\mathcal{M}$, where $m < n$. Current probabilistic models represent this manifold by mapping an $m$-dimensional latent variable through a neural network $f_\theta: \mathbb{R}^m \to \mathbb{R}^n$. Such proced"
3,859646c55f,Learning a Manifold as an Atlas,Nikolaos Pitelis; Chris Russell; Lourdes Agapito,2013,CVPR 2013,main,Poster,,,0,14.567,0.000,,https://openaccess.thecvf.com/content_cvpr_2013/html/Pitelis_Learning_a_Manifold_2013_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2013/papers/Pitelis_Learning_a_Manifold_2013_CVPR_paper.pdf,offline_cvpr,,"In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simul"
4,G5g6tDg1ZE,Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion,,2026,ICLR 2026,main,Active,generative models,Diffusion models;Conditional generation;Tabular diffusion;Manifold learning,0,14.215,0.000,,https://openreview.net/forum?id=G5g6tDg1ZE,,offline_iclr,,"Generating tabular data under conditions is critical to applications requiring precise control over the generative process. Existing methods rely on training-time strategies that do not generalise to unseen constraints during inference, and struggle to handle conditional tasks beyond tabular imputat"
5,peFP9Pl-6-_,Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent,Ruqi Zhang; qiang liu; Xin T. Tong,2022,NIPS 2022,main,Accept,,,0,14.072,0.000,,https://nips.cc/virtual/2022/poster/53704,https://openreview.net/pdf?id=peFP9Pl-6-_,offline_nips," We propose a variational framework for sampling in general constrained domains, with theoretical guarantees and practical algorithms.","Sampling methods, as important inference and learning techniques, are typically designed for unconstrained domains. However, constraints are ubiquitous in machine learning problems, such as those on safety, fairness, robustness, and many other properties that must be satisfied to apply sampling resu"
6,ad_F_z27pCx,A Discussion On the Validity of Manifold Learning,Dai Shi; Andi Han; Yi Guo; Junbin Gao,2022,ICLR 2022,main,Withdraw,,Manifold learning;Dimensionality Reduction;Computational Geometry;Simplicial Complex,0,13.645,0.000,,https://openreview.net/forum?id=ad_F_z27pCx,,offline_iclr,,"Dimensionality reduction (DR) and manifold learning (ManL) have been applied extensively in many machine learning tasks, including signal processing, speech recognition, and neuroinformatics. However, the understanding of whether DR and ManL models can generate valid learning results remains unclear"
7,kkvqVRu2Zy,Constrained Diffusion for Protein Design with Hard Structural Constraints,,2026,ICLR 2026,main,Active,"applications to physical sciences (physics, chemistry, biology, etc.)",Constrained Diffusion;Generative Models;Protein Design;Proximal Optimization;Motif Scaffolding,0,13.645,0.000,,https://openreview.net/forum?id=kkvqVRu2Zy,,offline_iclr,,"Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches observe critical failure modes when precise constraints are necessary for functional design. To this end, we present a "
8,ZTZa78mCbie,"For Manifold Learning, Deep Neural Networks Can be Locality Sensitive Hash Functions",Nishanth Dikkala; Gal Kaplun; Rina Panigrahy,2022,ICLR 2022,main,Withdraw,,theory of deep learning;theory of representation learning;manifold learning;locality sensitive hash functions;interpretability,0,13.609,0.000,,https://openreview.net/forum?id=ZTZa78mCbie,,offline_iclr,,"It is well established that training deep neural networks gives useful representations that capture essential features of the inputs. However, these representations are poorly understood in theory and practice. In the context of supervised learning an important question is whether these representati"
9,40f1211589,An Information Geometry of Statistical Manifold Learning,Ke Sun; Stéphane Marchand-Maillet,2014,ICML 2014,main,Poster,,,0,13.588,0.000,,https://proceedings.mlr.press/v32/suna14.html,http://proceedings.mlr.press/v32/suna14.pdf,offline_icml,,Manifold learning seeks low-dimensional representations of high-dimensional data. The main tactics have been exploring the geometry in an input data space and an output embedding space. We develop a manifold learning theory in a hypothesis space consisting of models. A model means a specific instanc
10,dJUb9XRoZI,Constrained Diffusion with Trust Sampling,William Huang; Yifeng Jiang; Tom Van Wouwe; Karen Liu,2024,NIPS 2024,main,Poster,diffusion_based_models,diffusion models;guidance;image generation;human motion,0,13.571,0.000,,https://neurips.cc/virtual/2024/poster/94344,https://openreview.net/pdf?id=dJUb9XRoZI,offline_nips,,"Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of con"
11,h6EWbx5xTj7,Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory,Zeru Zhang; Jiayin Jin; Zijie Zhang; Yang Zhou; Xin Zhao,2021,NIPS 2021,main,Poster,,Lottery Ticket Hypothesis;neural network pruning;dynamical systems;inertial manifold;theoretical evidence,0,13.527,0.000,,https://nips.cc/virtual/2021/poster/26292,https://openreview.net/pdf?id=h6EWbx5xTj7,offline_nips,Theoretically verify the precondition and validity of the Lottery Ticket Hypothesis,"Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning strategies are hard to guarantee that the pruned networks achieve test accuracy comparable to the original dense ones. R"
12,XFCKEgGhEK,Enhancing Cross-Lingual and Cross-Domain Adaptability in Large Language Models for Software Engineering,Yuanhao Li; Haocheng Yang; Wei Tan; Shilong Yuan; Hongbo Wang,2025,ICLR 2025,main,Reject,"transfer learning, meta learning, and lifelong learning",Code Generation;Transfer learning,0,13.238,0.000,,https://openreview.net/forum?id=XFCKEgGhEK,,offline_iclr,,"This paper presents a groundbreaking mathematical framework for unsupervised domain adaptation (UDA) in the context of cross-lingual and cross-domain code modeling. We introduce the Enhanced Dynamic Code Modeling (UDA-EDCM) system, which leverages advanced concepts from measure theory, differential "
13,yV2bsMVfal,RoSE: Enhancing SE(3)-based Protein Backbone Generation via Robust Score Estimation,Minzhang Li; Haochen Wang; Weichen Qin; Yifan Qin; Jiakai Zhang,2026,ICLR 2026,main,Withdraw,"applications to physical sciences (physics, chemistry, biology, etc.)",Riemannian Diffusion;Protein Design,0,13.209,0.000,,https://openreview.net/forum?id=yV2bsMVfal,,offline_iclr,,"This work presents improvements to Riemannian diffusion models for protein structure generation by developing robust heat kernel computation methods on $SE(3)$ space. While existing approaches suffer from approximation errors in score-based diffusion, our method enables stable and accurate denoising"
14,1222a1b772,More About VLAD: A Leap From Euclidean to Riemannian Manifolds,Masoud Faraki; Mehrtash T. Harandi; Fatih Porikli,2015,CVPR 2015,main,Poster,,,0,13.181,0.000,,https://openaccess.thecvf.com/content_cvpr_2015/html/Faraki_More_About_VLAD_2015_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2015/papers/Faraki_More_About_VLAD_2015_CVPR_paper.pdf,offline_cvpr,,This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds. We provide a comprehensive mathematical framework that formulates the aggregation problem of such manifold dat
15,17864,Manifold structure in graph embeddings,Patrick Rubin-Delanchy,2020,NIPS 2020,main,Spotlight,,,0,13.164,0.000,,https://nips.cc/virtual/2020/poster/17864,https://papers.nips.cc/paper_files/paper/2020/file/8682cc30db9c025ecd3fee433f8ab54c-Paper.pdf,offline_nips,,"Statistical analysis of a graph often starts with embedding, the process of representing its nodes as points in space. How to choose the embedding dimension is a nuanced decision in practice, but in theory a notion of true dimension is often available. In spectral embedding, this dimension may be ve"
16,7Cxk63lTTm,A Manifold Perspective on the Statistical Generalization of Graph Neural Networks,Zhiyang Wang; Juan Cervino; Alejandro Ribeiro,2025,ICML 2025,main,Poster,deep_learning->graph_neural_networks,graph neural networks;generalization,0,13.115,0.000,,https://icml.cc/virtual/2025/poster/46337,https://openreview.net/pdf?id=7Cxk63lTTm,offline_icml,,"Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs. Despite their impressive performances in various graph learning tasks, the theoretical understanding of their generalization capability is still lacking. Previous GNN generalization bounds ignore the underlying g"
17,dpnPOXoqVQ,S$^2$MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection,Xuelin Zhang; Hong Chen; Yingjie Wang; Zeyu Zhang; Tieliang Gong,2025,ICLR 2025,main,Reject,learning theory,manifold regularization;bilevel optimization;sparse additive model;robustness;learning theory,0,13.010,0.000,,https://openreview.net/forum?id=dpnPOXoqVQ,,offline_iclr,,"Semi-supervised learning with manifold regularization is a classical family for learning from the labeled and unlabeled data jointly, where the key requirement is the support of unknown marginal distribution enjoys the geometric structure of a Riemannian manifold. Usually, the Laplace-Beltrami opera"
18,KJ3zkHzsKm,Constrained Reinforcement Learning using Bender’s Decomposition and Exact Constraint Satisfaction,Alexander Mattick; Christopher Mutschler,2026,ICLR 2026,main,Withdraw,reinforcement learning,Reinforcement Learning;Constrained Reinforcement Learning;Optimization,0,12.998,0.000,,https://openreview.net/forum?id=KJ3zkHzsKm,,offline_iclr,,"Recent advancements in reinforcement learning (RL) have expanded its applications beyond sequential decision-making to encompass non-sequential tasks, such as matrix decompositions, automatic generation of sorting networks, and combinatorial optimization. However, these tasks often require problem-s"
19,Tubzedlc4P,A Statistical Manifold Framework for Point Cloud Data,Yonghyeon Lee; Seungyeon Kim; Jinwon Choi; Frank C. Park,2022,ICLR 2022,main,Reject,,Riemannian Geometry;Point Cloud;Autoencoders,0,12.921,0.000,,https://openreview.net/forum?id=Tubzedlc4P,,offline_iclr,,"A large class of problems in machine learning involve data sets in which each data point is a point cloud in $\mathbb{R}^D$. The reason that most machine learning algorithms designed for point cloud data tend to be ad hoc, and difficult to measure their performance in a uniform and quantitative way,"
20,Lv3MfAEgvVv,Hyperbolic Binary Neural Network,Jun Chen; Jingyang Xiang; Tianxin Huang; Xiangrui Zhao; Yong Liu,2023,ICLR 2023,main,Withdraw,,Neural network quantization;Hyperbolic geometry;Riemannian manifold,0,12.912,0.000,,https://openreview.net/forum?id=Lv3MfAEgvVv,,offline_iclr,We propose a Hyperbolic Binary Neural Network that updates the parameters in hyperbolic space.,"Binary Neural Network (BNN) converts the full-precision weights and activations to the extreme 1-bit counterparts, which is especially suitable to be deployed on lightweight mobile devices. Neural network binarization is usually formulated as a constrained optimization problem, which restricts its o"
21,rJeBJJBYDB,Chart Auto-Encoders for Manifold Structured Data,Stephan Schonsheck; Jie Chen; Rongjie Lai,2020,ICLR 2020,main,Reject,,Auto-encoder;differential manifolds;multi-charted latent space,0,12.895,0.000,,https://openreview.net/forum?id=rJeBJJBYDB,,offline_iclr,Manifold-structured latent space for generative models," Auto-encoding and generative models have made tremendous successes in image and signal representation learning and generation. These models, however, generally employ the full Euclidean space or a bounded subset (such as $[0,1]^l$) as the latent space, whose trivial geometry is often too simplistic"
22,4723,Positive Curvature and Hamiltonian Monte Carlo,Christof Seiler; Simon Rubinstein-Salzedo; Susan Holmes,2014,NIPS 2014,main,Poster,,,0,12.887,0.000,,https://nips.cc/virtual/2014/poster/4723,https://papers.nips.cc/paper_files/paper/2014/file/c76d3b26eba4f2c2fed5695faaae774f-Paper.pdf,offline_nips,,"The Jacobi metric introduced in mathematical physics can be used to analyze Hamiltonian Monte Carlo (HMC). In a geometrical setting, each step of HMC corresponds to a geodesic on a Riemannian manifold with a Jacobi metric. Our calculation of the sectional curvature of this HMC manifold allows us to "
23,TBJIf2M23q,Rethinking Large Language Model Distillation: A Constrained Markov Decision Process Perspective,Matthieu Zimmer; Xiaotong Ji; Tu Nguyen; Haitham Bou Ammar,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",distillation;reinforcement learning;large language models;constrained reinforcement learning,0,12.746,0.000,,https://openreview.net/forum?id=TBJIf2M23q,,offline_iclr,,"We introduce a novel approach to large language model (LLM) distillation by formulating it as a constrained reinforcement learning problem. While recent work has begun exploring the integration of task-specific rewards into distillation processes, existing methods typically rely on ad-hoc reward wei"
24,3776,Manifold Mixup: Better Representations by Interpolating Hidden States,Vikas Verma; Alex Lamb; Christopher Beckham; Amir Najafi; Ioannis Mitliagkas,2019,ICML 2019,main,Oral,,,0,12.726,0.000,,https://icml.cc/virtual/2019/poster/3776,http://proceedings.mlr.press/v97/verma19a/verma19a.pdf,offline_icml,,"Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose \manifoldmixup{}, a simple re"
25,18845,Sample complexity and effective dimension for regression on manifolds,Andrew McRae; Justin Romberg; Mark Davenport,2020,NIPS 2020,main,Poster,,,0,12.592,0.000,,https://nips.cc/virtual/2020/poster/18845,https://papers.nips.cc/paper_files/paper/2020/file/977f8b33d303564416bf9f4ab1c39720-Paper.pdf,offline_nips,,"We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and our goal is to help understand the effectiveness of various implicit and explicit dimensionality-reduction methods that "
26,ntV5xZfzEk,Constrained Binary Decision Making,Daniel Průša; Vojtech Franc,2024,NIPS 2024,main,Poster,probabilistic_methods,binary statistical decision making;constrained optimization;Neyman-Pearson problem;selective classification,0,12.581,0.000,,https://neurips.cc/virtual/2024/poster/93660,https://openreview.net/pdf?id=ntV5xZfzEk,offline_nips,,"Binary statistical decision making involves choosing between two states based on statistical evidence. The optimal decision strategy is typically formulated through a constrained optimization problem, where both the objective and constraints are expressed as integrals involving two Lebesgue measurab"
27,H139Q_gAW,Learning Graph Convolution Filters from Data Manifold,Guokun Lai; Hanxiao Liu; Yiming Yang,2018,ICLR 2018,main,Reject,,Label Propagation;Depthwise separable convolution;Graph and geometric convolution,0,12.523,0.000,,https://openreview.net/forum?id=H139Q_gAW,,offline_iclr,"We devise a novel Depthwise Separable Graph Convolution (DSGC) for the generic spatial domain data, which is highly compatible with depthwise separable convolution.","Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and "
28,21803,DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network,Xuan Shen; Yaohua Wang; Ming Lin; Yilun Huang; Hao Tang,2023,CVPR 2023,main,Poster,,,0,12.396,0.000,,https://cvpr.thecvf.com/virtual/2023/poster/21803,https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_DeepMAD_Mathematical_Architecture_Design_for_Deep_Convolutional_Neural_Network_CVPR_2023_paper.pdf,offline_cvpr,,"The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT mod"
29,HYWdlCPtao,Curvature Enhanced Manifold Sampling,Ilya Kaufman; Omri Azencot,2025,ICLR 2025,main,Reject,"other topics in machine learning (i.e., none of the above)",Manifold learning;Data augmentation;Regression,0,12.372,0.000,,https://openreview.net/forum?id=HYWdlCPtao,,offline_iclr,,"Over-parameterized deep learning models, characterized by their large number of parameters, have demonstrated remarkable performance in various tasks. Despite the potential risk of overfitting, these models often generalize well to unseen data due to effective regularization techniques, with data au"
30,5L8XMh667qz,Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations,Sanjukta Krishnagopal; Jacob Bedrossian,2021,ICLR 2021,main,Reject,,VAE;sliced Wasserstein distance;latent representation;interpolation;manifold embedding;geodesics;network algorithm,0,12.171,0.000,,https://openreview.net/forum?id=5L8XMh667qz,,offline_iclr,,"While variational autoencoders have been successful in a variety of tasks, the use of conventional Gaussian or Gaussian mixture priors are limited in their ability to encode underlying structure of data in the latent representation.
In this work, we introduce an Encoded Prior Sliced Wasserstein Auto"
31,kCUDzyKQ7G,Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold,Xinghan Li; Haodong Wen; Kaifeng Lyu,2025,NIPS 2025,main,Poster,theory,Adam;adaptive gradient methods;implicit bias;regularization;deep learning theory,0,12.158,0.000,,https://openreview.net/forum?id=kCUDzyKQ7G,,offline_nips,,"Despite the popularity of Adam optimizer in practice, most theoretical analyses study SGD as a proxy and little is known about how the solutions found by Adam differ. In this paper, we show that Adam reduces a specific form of sharpness measure shaped by its adaptive updates, leading to qualitativel"
32,4bNGE4WSfJ,Learning Globally Smooth Functions on Manifolds,Juan Cervino; Luiz F. O. Chamon; Benjamin David Haeffele; Rene Vidal; Alejandro Ribeiro,2023,ICML 2023,main,Poster,,,0,12.149,0.000,,https://icml.cc/virtual/2023/poster/24397,https://openreview.net/pdf?id=4bNGE4WSfJ,offline_icml,,Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. This work combines techniques from semi-infinite constrained learning and manifold regularization to learn representations that are globally smooth on a manifold. To do 
33,a0kq0tJwwn,The Momentum Persistence Effect: A New Theory for Why Soft Constraints Outperform Hard Projections,Khurram Khalil; Ripan Kumar Kundu; Khaza Anuarul Hoque,2026,ICLR 2026,main,Withdraw,optimization,Constrained Optimization;Deep Learning Theory;Optimization Dynamics;Momentum Methods;Orthogonal Constraints;Regularization;Stiefel Manifold,0,11.966,0.000,,https://openreview.net/forum?id=a0kq0tJwwn,,offline_iclr,,"A persistent empirical puzzle in deep learning is why soft, penalty-based constraints often outperform their mathematically exact, hard-projected counterparts. While classical optimization theory provides elegant models, it fails to explain this phenomenon. This paper resolves the mystery by identif"
34,8ba6358e96,Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds,Nitesh Shroff; Pavan Turaga; Rama Chellappa,2011,NIPS 2011,main,Poster,,,0,11.861,0.000,,https://papers.nips.cc/paper_files/paper/2011/hash/d1f491a404d6854880943e5c3cd9ca25-Abstract.html,https://papers.nips.cc/paper_files/paper/2011/file/d1f491a404d6854880943e5c3cd9ca25-Paper.pdf,offline_nips,,"In this paper, we consider the 'Precis' problem of sampling K representative yet diverse data points from a large dataset. This problem arises frequently in applications such as video and document summarization, exploratory data analysis, and pre-filtering. We formulate a general theory which encomp"
35,uQiFsBil3p,Random matrix theory improved Fréchet mean of symmetric positive definite matrices,Florent Bouchard; Ammar Mian; Malik Tiomoko; Guillaume Ginolhac; Frederic Pascal,2024,ICML 2024,main,Poster,,,0,11.852,0.000,,https://icml.cc/virtual/2024/poster/32828,https://openreview.net/pdf?id=uQiFsBil3p,offline_icml,,"In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fréchet means on the manifold of symmetric positive definite matrices, commonly referred to as Karcher or geometric means. Such means are leveraged in numerous machine learning tasks. "
36,eBS3dQQ8GV,Emergence of meta-stable clustering in mean-field transformer models,Giuseppe Bruno; Federico Pasqualotto; Andrea Agazzi,2025,ICLR 2025,main,Oral,"foundation or frontier models, including LLMs",Mean-field limits;Transformers;Meta-stability;Clustering,0,11.834,0.000,,https://iclr.cc/virtual/2025/poster/28944,https://openreview.net/pdf?id=eBS3dQQ8GV,offline_iclr,,"We model the evolution of tokens within a deep stack of Transformer layers as a continuous-time flow on the unit sphere, governed by a mean-field interacting particle system, building on the framework introduced in Geshkovski et al. (2023). Studying the corresponding mean-field Partial Differential "
37,6413,Option Discovery in the Absence of Rewards with Manifold Analysis,Amitay Bar; Ronen Talmon; Ron Meir,2020,ICML 2020,main,Poster,,,0,11.796,0.000,,https://icml.cc/virtual/2020/poster/6413,http://proceedings.mlr.press/v119/bar20a/bar20a.pdf,offline_icml,,"Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning. In this paper, we present an approach based on spectral graph theory and derive an algorithm that systematically discovers options without access to a specific reward or task as"
38,11994,The Sparse Manifold Transform,Yubei Chen; Dylan Paiton; Bruno Olshausen,2018,NIPS 2018,main,Poster,,,0,11.779,0.000,,https://nips.cc/virtual/2018/poster/11994,https://papers.nips.cc/paper_files/paper/2018/file/8e19a39c36b8e5e3afd2a3b2692aea96-Paper.pdf,offline_nips,,"We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embeddi"
39,8985,Density Constrained Reinforcement Learning,Zengyi Qin; Yuxiao Chen; Chuchu Fan,2021,ICML 2021,main,Spotlight,,,0,11.755,0.000,,https://icml.cc/virtual/2021/poster/8985,http://proceedings.mlr.press/v139/qin21a/qin21a.pdf,offline_icml,,"We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works. State density has a clear physical and mathematical interpretation, and is able to express a wide varie"
40,rhdfTOiXBng,NaturalProver: Grounded Mathematical Proof Generation with Language Models,Sean Welleck; Jiacheng Liu; Ximing Lu; Hannaneh Hajishirzi; Yejin Choi,2022,NIPS 2022,main,Accept,,language modeling;reasoning;neural theorem proving,0,11.696,0.000,,https://nips.cc/virtual/2022/poster/53914,https://openreview.net/pdf?id=rhdfTOiXBng,offline_nips,,"Theorem proving in natural mathematical language – the mixture of symbolic and natural language used by humans – plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models."
41,vJb4I2ANmy,Noisy Feature Mixup,Soon Hoe Lim; N. Benjamin Erichson; Francisco Utrera; Winnie Xu; Michael W. Mahoney,2022,ICLR 2022,main,Poster,,Data augmentation;implicit regularization;mixup;noise injection;model robustness,0,11.657,0.000,,https://iclr.cc/virtual/2022/poster/6227,https://openreview.net/pdf?id=vJb4I2ANmy,offline_iclr,,"We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes. Rather than training with convex combinations of pairs of examples and their labels, we use noise-perturbed convex com"
42,m7zsaLt1Sab,Finding One Missing Puzzle of Contextual Word Embedding: Representing Contexts as Manifold,Hailin Hu; Rong Yao; Cheng LI,2022,ICLR 2022,main,Reject,,Contextual Word Embedding;Category Theory;Manifold,0,11.656,0.000,,https://openreview.net/forum?id=m7zsaLt1Sab,,offline_iclr,,"The current understanding of contextual word embedding interprets the representation by associating each token to a vector that is dynamically modulated by the context. However, this “token-centric” understanding does not explain how a model represents context itself, leading to a lack of characteri"
43,OEPP8T0zUX,TopoAlign: A Framework for Aligning Code to Math via Topological Decomposition,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",autoformalisation;formal reasoning;code data,0,11.652,0.000,,https://openreview.net/forum?id=OEPP8T0zUX,,offline_iclr,,"Large Language Models (LLMs) excel at both informal and formal (e.g. Lean 4) mathematical reasoning but still struggle with autoformalisation, the task of transforming informal into formal mathematical statements. Autoformalisation helps pair the informal reasoning of LLMs with formal proof assistan"
44,30aSE3FB3L,Matrix Manifold Neural Networks++,Xuan Son Nguyen; Shuo Yang; Aymeric Histace,2024,ICLR 2024,main,Poster,"representation learning for computer vision, audio, language, and other modalities",manifold learning;representation learning;gyrovector spaces;deep learning,0,11.636,0.000,,https://iclr.cc/virtual/2024/poster/19532,https://openreview.net/pdf?id=30aSE3FB3L,offline_iclr,,"Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contr"
45,sl_0rQmHxQk,Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation,Florian Bernard; Daniel Cremers; Anders Johan Thunberg,2021,NIPS 2021,main,Poster,,Stiefel manifold;quadratic optimisation;permutation synchronisation;sparsity;multi-matching;correspondence problems;manifold optimisation;QR decomposition;orthogonal iteration algorithm,0,11.631,0.000,,https://nips.cc/virtual/2021/poster/28158,https://openreview.net/pdf?id=sl_0rQmHxQk,offline_nips,A method for finding a globally optimal solution of a quadratic objective function over the Stiefel manifold that is sparse.,We address the non-convex optimisation problem of finding a sparse matrix on the Stiefel manifold (matrices with mutually orthogonal columns of unit length) that maximises (or minimises) a quadratic objective function. Optimisation problems on the Stiefel manifold occur for example in spectral relax
46,eAFNJk63KE,Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations,Larkin Liu; Yutong Chao; Jalal Etesami; Kashif Rasul,2025,ICLR 2025,main,Reject,learning on graphs and other geometries & topologies,Neural Normalizing Flows;Stackelberg Games;Riemannian Manifolds,0,11.600,0.000,,https://openreview.net/forum?id=eAFNJk63KE,,offline_iclr,,"We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the core of this approach is a learned diffeomorphism that maps the joint action space to a smooth Riemannian manifold, refer"
47,YZdc7mTq7I,GeoMind: A Geometric Neural Network of State Space Model for Understanding Brain Dynamics on Riemannian Manifold,Tingting Dan; Jiaqi Ding; Guorong Wu,2025,ICLR 2025,main,Withdraw,applications to neuroscience & cognitive science,Geometric deep learning;state space model;brain dynamics;Riemannian Manifold,0,11.432,0.000,,https://openreview.net/forum?id=YZdc7mTq7I,,offline_iclr,,State space model (SSM) is a powerful tool in neuroscience field to characterize the dynamic nature of brain functions by elucidating the mechanism of how brain system transits between brain states and how underlying states give rise to the observed neural activities. Although tremendous efforts hav
48,vKMVrqvXbXu,Effects of Data Geometry in Early Deep Learning,Saket Tiwari; George Konidaris,2022,ICLR 2022,main,Reject,,Deep learning;geometry;manifolds;deep learning theory,0,11.397,0.000,,https://openreview.net/forum?id=vKMVrqvXbXu,,offline_iclr,,"Deep neural networks can approximate functions on different types of data, from images to graphs, with varied underlying structure.This underlying structure can be viewed as the geometry of the data manifold. By extending recent advances in the theoretical understanding of neural networks, we study "
49,4n1PS9WvdYv,On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds,Biraj Dahal; Alexander Havrilla; Minshuo Chen; Tuo Zhao; Wenjing Liao,2022,NIPS 2022,main,Accept,,Deep generative models;distribution estimation;low-dimensional manifold,0,11.384,0.000,,https://nips.cc/virtual/2022/poster/53839,https://openreview.net/pdf?id=4n1PS9WvdYv,offline_nips,We prove approximation and statistical estimation theories of deep generative models for distribution learning when the distribution is supported on a low-dimensional manifold.,"Deep generative models have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that deep generative networks can efficiently generate high-dimensional complex data from a low-dimensional easy-to-sample distribution. However, this phenomenon ca"
50,5b5wZg6Zeo,A solvable model of learning generative diffusion: theory and insights,Hugo Cui; Cengiz Pehlevan; Yue M. Lu,2025,NIPS 2025,main,Poster,theory,high-dimensional asymptotics;statistical physics;diffusion model,0,11.352,0.000,,https://openreview.net/forum?id=5b5wZg6Zeo,,offline_nips,,"In this manuscript, we analyze a solvable model of flow or diffusion-based generative model. We consider the problem of learning a model parametrized by a two-layer auto-encoder, trained with online stochastic gradient descent, on a high-dimensional target density with an underlying low-dimensional "
51,c2OtbtZXFC,Retraction-free optimization over the Stiefel manifold with application to the LoRA fine-tuning,Yuan Zhang; Jiang Hu; Jiaxi Cui; Lin Lin; Zaiwen Wen,2025,ICLR 2025,main,Withdraw,optimization,landing;manifold;fine-tuning;LoRA,0,11.348,0.000,,https://openreview.net/forum?id=c2OtbtZXFC,,offline_iclr,,"Optimization over the Stiefel manifold has played a significant role in various machine learning tasks. Many existing algorithms either use the retraction operator to keep each iterate staying on the manifold, or solve an unconstrained quadratic penalized problem. The retraction operator in the form"
52,TXJ7vLgOS4,BoostStep: Boosting Mathematical Capability of Large Language Models via Step-aligned In Context Learning,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Mathematical Reasoning;Large Language Models;In-context Learning,0,11.328,0.000,,https://openreview.net/forum?id=TXJ7vLgOS4,,offline_iclr,,"Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed in-context learning (ICL) examples. However, this potential is often constrained by two major challenges in ICL: granula"
53,e979a4d03a,Learning Multiple Tasks using Manifold Regularization,Arvind Agarwal; Samuel Gerber; Hal Daume,2010,NIPS 2010,main,Poster,,,0,11.325,0.000,,https://papers.nips.cc/paper_files/paper/2010/hash/2cbca44843a864533ec05b321ae1f9d1-Abstract.html,https://papers.nips.cc/paper_files/paper/2010/file/2cbca44843a864533ec05b321ae1f9d1-Paper.pdf,offline_nips,,We present a novel method for multitask learning (MTL) based on {\it manifold regularization}: assume that all task parameters lie on a manifold. This is the generalization of a common assumption made in the existing literature: task parameters share a common {\it linear} subspace. One proposed meth
54,CJY7NEXVwC,A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications,Yanbo Chen; Weiwei Liu,2023,NIPS 2023,main,Poster,,Learning Theory,0,11.307,0.000,,https://nips.cc/virtual/2023/poster/72434,https://openreview.net/pdf?id=CJY7NEXVwC,offline_nips,,"Transfer-based attacks are a practical method of black-box adversarial attacks, in which the attacker aims to craft adversarial examples from a source (surrogate) model that is transferable to the target model. A wide range of empirical works has tried to explain the transferability of adversarial e"
55,zJaqyxO7K7,CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images,Chengqi Duan; Kaiyue Sun; Rongyao Fang; Manyuan Zhang; Yan Feng,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Multimodal Large Language Models;Mathematical Reasoning;Thinking with Images;Multimodal Benchmark,0,11.306,0.000,,https://openreview.net/forum?id=zJaqyxO7K7,,offline_iclr,,"Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottleneck with problems requiring visual assistance, such as drawing auxiliary lines or plotting functions to solve the problem"
56,Yw7ZNeDVpBS,BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain,Zhao Tang Luo; Huiyan Sang; Bani Mallick,2021,NIPS 2021,main,Poster,,Bayesian nonparametric regression;Constrained domain;Ensemble learning;Manifold;Random spanning trees,0,11.250,0.000,,https://nips.cc/virtual/2021/poster/28133,https://openreview.net/pdf?id=Yw7ZNeDVpBS,offline_nips,This paper proposes a Bayesian nonparametric regression model on manifolds via additive random spanning tree partitions that adapts to different smoothness levels while respecting intrinsic geometries.,"Nonparametric regression on complex domains has been a challenging task as most existing methods, such as ensemble models based on binary decision trees, are not designed to account for intrinsic geometries and domain boundaries. This article proposes a Bayesian additive regression spanning trees (B"
57,083947b5c2,A unifying framework for vector-valued manifold regularization and multi-view learning,Minh Hà Quang; Loris Bazzani; Vittorio Murino,2013,ICML 2013,main,Poster,,,0,11.231,0.000,,https://proceedings.mlr.press/v28/haquang13.html,http://proceedings.mlr.press/v28/haquang13.pdf,offline_icml,,"This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency  between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special "
58,7n8RzGQKnR,A Symbolic Framework for Evaluating Mathematical Reasoning with Transformers,Jordan Meadows; Marco Valentino; Damien Teney; Andre Freitas,2024,ICLR 2024,main,Withdraw,datasets and benchmarks,mathematical reasoning;generalisation;gpt;bert;sequence classification;synthetic data;fine-tuning;few-shot learning,0,11.231,0.000,,https://openreview.net/forum?id=7n8RzGQKnR,,offline_iclr,,"This paper proposes a methodology for generating synthetic mathematical derivations via a computer algebra system to evaluate the generalisability of Transformers in symbolic and quantitative reasoning problems, and provides a general framework for building large-scale and high-quality benchmarks in"
59,49Rc51iCso,Mitigating Overthinking in Large Reasoning Models via Manifold Steering,Yao Huang; Huanran Chen; Shouwei Ruan; Yichi Zhang; Xingxing Wei,2025,NIPS 2025,main,Poster,deep_learning,Large Reasoning Models;Overthinking;Mechanistic Interpretability;Manifold Steering,0,11.206,0.000,,https://openreview.net/forum?id=49Rc51iCso,,offline_nips,,"Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently exhibit a phenomenon known as *overthinking* during inference, characterized by excessive validation loops and redundan"
60,AJN5btaqNk,Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows,Willem Diepeveen; Georgios Batzolis; Zakhar Shumaylov; Carola-Bibiane Schönlieb,2025,ICML 2025,main,Poster,deep_learning->generative_models_and_autoencoders,data manifold geometry;score-based pullback Riemannian metric;anisotropic normalizing flows;closed-form geodesics;intrinsic dimension estimation;Riemannian auto-encoder;interpretable representation learning,0,11.153,0.000,,https://icml.cc/virtual/2025/poster/46179,https://openreview.net/pdf?id=AJN5btaqNk,offline_icml,,"Data-driven Riemannian geometry has emerged as a powerful tool for interpretable representation learning, offering improved efficiency in downstream tasks. Moving forward, it is crucial to balance cheap manifold mappings with efficient training algorithms. In this work, we integrate concepts from pu"
61,89764a0f6a,Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem,Rui Caseiro; Pedro Martins; Joao F. Henriques; Fatima Silva Leite; Jorge Batista,2013,CVPR 2013,main,Poster,,,0,11.115,0.000,,https://openaccess.thecvf.com/content_cvpr_2013/html/Caseiro_Rolling_Riemannian_Manifolds_2013_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2013/papers/Caseiro_Rolling_Riemannian_Manifolds_2013_CVPR_paper.pdf,offline_cvpr,,"In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27]. A popular framework, valid over any Riemannian manifold, was proposed in [31] for binary classification. Once moving from binary to multi-class "
62,B1uvH_gC-,Parametric Manifold Learning Via Sparse Multidimensional Scaling,Gautam Pai; Ronen Talmon; Ron Kimmel,2018,ICLR 2018,main,Reject,,Manifold Learning;Non-linear Dimensionality Reduction;Neural Networks;Unsupervised Learning,0,11.096,0.000,,https://openreview.net/forum?id=B1uvH_gC-,,offline_iclr,Parametric Manifold Learning with Neural Networks in a Geometric Framework ,We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We employ Siamese networks to solve the problem of least squares multidimensional scaling for generating mappings that preserve geodesic distances 
63,gLPkzWjdhBN,Learning Iterative Neural Optimizers for Image Steganography,Xiangyu Chen; Varsha Kishore; Kilian Q Weinberger,2023,ICLR 2023,main,Poster,,,0,10.970,0.000,,https://iclr.cc/virtual/2023/poster/10886,https://openreview.net/pdf?id=gLPkzWjdhBN,offline_iclr,,"Image steganography is the process of concealing secret information in images through imperceptible changes. 
Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natu"
64,yxj33c6NuX,Minimum Curvature Manifold Learning,Yonghyeon Lee; Frank C. Park,2023,ICLR 2023,main,Reject,,Autoencoder;Manifold;Curvature;Riemannian geometry,0,10.953,0.000,,https://openreview.net/forum?id=yxj33c6NuX,,offline_iclr,"We propose a minimum extrinsic curvature principle for manifold regularization and Minimum Curvature Autoencoder (MCAE), a graph-free coordinate-invariant extrinsic curvature minimization framework for autoencoder regularization.","It is widely observed that vanilla autoencoders can have low manifold learning accuracy given a noisy or small training dataset. 
Recent work has discovered that it is important to regularize the decoder that explicitly parameterizes the manifold, 
where a neighborhood graph is employed for decoder "
65,ad8c5bbd1c,Sparse Representation Classification With Manifold Constraints Transfer,Baochang Zhang; Alessandro Perina; Vittorio Murino; Alessio Del Bue,2015,CVPR 2015,main,Poster,,,0,10.928,0.000,,https://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.pdf,offline_cvpr,,The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework tha
66,aWXrVm07Zl,Neural Superposition Networks,Atiyo Ghosh; Nicolò Toscano; Jongyeong Lee; Hyukgeun Cha; Jun-Ho Lee,2025,NIPS 2025,main,Reject,machine_learning_for_sciences,differential equations;physics-informed neural networks;scientific machine learning;differentially constrained architecture;principle of superposition,0,10.853,0.000,,https://openreview.net/forum?id=aWXrVm07Zl,,offline_nips,,"We introduce _Neural Superposition Networks_, a class of physics-constrained neural architectures that exactly satisfy given partial differential equations (PDEs) by construction. In contrast to traditional physics-informed neural networks (PINNs), which enforce PDE constraints via loss regularizati"
67,rJlJF1SYPB,Universality Theorems for Generative Models,Valentin Khrulkov; Ivan Oseledets,2020,ICLR 2020,main,Withdraw,,generative models;theory;universality;manifolds;differential geometry,0,10.844,0.000,,https://openreview.net/forum?id=rJlJF1SYPB,,offline_iclr,We shot that a wide class of manifolds can be generated by ReLU and sigmoid networks with arbitrary precision.,"Despite the fact that generative models are extremely successful in practice, the theory underlying this phenomenon is only starting to catch up with practice. In this work we address the question of the universality of generative models: is it true that neural networks can approximate any data mani"
68,BlCnycxgJQ,An Inexact Regularized Adaptive Algorithm with Manifold Identification for Training Structured Neural Networks,Zih-Syuan Huang; Ching-pei Lee,2024,ICLR 2024,main,Reject,optimization,Deep learning;structured models;adaptive method;manifold identification;variance reduction;inexact subproblem solution,0,10.832,0.000,,https://openreview.net/forum?id=BlCnycxgJQ,,offline_iclr,,"We propose an inexact regularized adaptive dual averaging algorithm with momentum, RAMDA, for training structured neural networks in various tasks with the help of regularization. Through the theory of manifold identification, we show that after a finite number of steps, the structures of the iterat"
69,A8ez8ThZWq,REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model,,2026,ICLR 2026,main,Active,interpretability and explainable AI,Interpretability;Large Language Models;Reasoning Manifold,0,10.821,0.000,,https://openreview.net/forum?id=A8ez8ThZWq,,offline_iclr,,"Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research.
To provide a measurable geometric analysis perspective, we define the concept of the **Reasoning Manifold**, a latent low-dimensional geometric structure"
70,,Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning,Xing Xu; Fumin Shen; Yang Yang; Dongxiang Zhang; Heng Tao Shen,2017,CVPR 2017,main,Poster,,,0,10.813,0.000,,,https://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_Matrix_Tri-Factorization_With_CVPR_2017_paper.pdf,offline_cvpr,,"Zero-shot learning (ZSL) aims to recognize objects of unseen classes with available training data from another set of seen classes. Existing solutions are focused on exploring knowledge transfer via an intermediate semantic embedding (e.g.s, attributes) shared between seen and unseen classes. In thi"
71,,Zero Shot Learning via Multi-Scale Manifold Regularization,Shay Deutsch; Soheil Kolouri; Kyungnam Kim; Yuri Owechko; Stefano Soatto,2017,CVPR 2017,main,Poster,,,0,10.771,0.000,,,https://openaccess.thecvf.com/content_cvpr_2017/papers/Deutsch_Zero_Shot_Learning_CVPR_2017_paper.pdf,offline_cvpr,,"We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs. Our inference approach includes a smoothness criterion for a function mapping nodes on a graph (visual representation) onto a linear space (semantic representation), which we "
72,zokEN0xOb0Q,Differential Privacy with Manifold Data Dependency,Lei Wang; Deming Yuan; Guodong Shi,2022,ICLR 2022,main,Withdraw,,Differential privacy;data correlation,0,10.763,0.000,,https://openreview.net/forum?id=zokEN0xOb0Q,,offline_iclr,,"In this paper, we study dataset processing mechanisms generated by linear queries in the presence of manifold data dependency. Specifically, the input data are assumed to lie in an affine manifold as prior knowledge known to adversaries. First of all, we show such manifold data dependency may have a"
73,Zvh6lF5b26N,Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold,Can Yaras; Peng Wang; Zhihui Zhu; Laura Balzano; Qing Qu,2022,NIPS 2022,main,Accept,,neural collapse;Riemannian manifold;feature normalization;nonconvex optimization,0,10.759,0.000,,https://nips.cc/virtual/2022/poster/54456,https://openreview.net/pdf?id=Zvh6lF5b26N,offline_nips,,"When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called ""neural collapse'"" phenomenon. More specifically, for the output features of the penultimate layer, for each class the within-class features converge to t"
74,4408,Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces,Hà Quang Minh; Marco San Biagio; Vittorio Murino,2014,NIPS 2014,main,Spotlight,,,0,10.754,0.000,,https://nips.cc/virtual/2014/poster/4408,https://papers.nips.cc/paper_files/paper/2014/file/3000e56b48442cd23b49e5064bf1a9e6-Paper.pdf,offline_nips,,"This paper introduces a novel mathematical and computational framework, namely {\it Log-Hilbert-Schmidt metric} between positive definite operators on a Hilbert space. This is a generalization of the Log-Euclidean metric on the Riemannian manifold of positive definite matrices to the infinite-dimens"
75,plAiJUFNja,Graph-Enhanced Learning for Predicting Optimal Drug Combinations Using Contrastive Embedding,Zhenghan chen; youhuan yang; Lang Zheng; Ruxue Xing; Han Quan,2025,ICLR 2025,main,Reject,"transfer learning, meta learning, and lifelong learning",Graph Learning;Contrastive Embedding;DDI,0,10.745,0.000,,https://openreview.net/forum?id=plAiJUFNja,,offline_iclr,,"We present a groundbreaking unified theory for drug-drug interaction (DDI) aware domain adaptation (DA) in the context of drug synergy prediction. Our framework seamlessly integrates concepts from optimal transport, information geometry, and quantum information theory within the setting of abstract "
76,JZ3Svjj9hG,Generative Counterfactual Manifold Perturbation: A Robust Framework for Treatment Effect Estimation with Unobserved Confounders,,2026,ICLR 2026,main,Active,causal reasoning,ML: Causal Learning,0,10.744,0.000,,https://openreview.net/forum?id=JZ3Svjj9hG,,offline_iclr,,"Estimating treatment effects from observational data is difficult when unobserved confounders create spurious associations that bias simple estimators. Recent generative approaches learn outcome distributions with conditional diffusion models, and some robust representation methods introduce sensiti"
77,f15e9021c5,Learning Manifolds with K-Means and K-Flats,Guillermo Canas; Tomaso Poggio; Lorenzo Rosasco,2012,NIPS 2012,main,Poster,,,0,10.721,0.000,,https://papers.nips.cc/paper_files/paper/2012/hash/b20bb95ab626d93fd976af958fbc61ba-Abstract.html,https://papers.nips.cc/paper_files/paper/2012/file/b20bb95ab626d93fd976af958fbc61ba-Paper.pdf,offline_nips,,"We study the problem of estimating a manifold from random samples. In particular, we consider piecewise constant and piecewise linear estimators induced by  k-means and k-flats, and analyze their performance. We extend previous results  for k-means in two separate directions. First, we provide new re"
78,YD9ZoqUDAY,Unified K-Means Clustering with Label-Guided Manifold Learning,Qianqian Wang; Mengping Jiang; Zhengming Ding; Quanxue Gao,2025,ICML 2025,main,Poster,general_machine_learning->clustering,Balanced clustering;unsupervised learning;low-pass filtering distance.,0,10.705,0.000,,https://icml.cc/virtual/2025/poster/44925,https://openreview.net/pdf?id=YD9ZoqUDAY,offline_icml,,"K-Means clustering is a classical and effective unsupervised learning method attributed to its simplicity and efficiency. However, it faces notable challenges, including sensitivity to random initial centroid selection, a limited ability to discover the intrinsic manifold structures within nonlinear"
79,18491,Learning Manifold Implicitly via Explicit Heat-Kernel Learning,Yufan Zhou; Changyou Chen; Jinhui Xu,2020,NIPS 2020,main,Poster,,,0,10.697,0.000,,https://nips.cc/virtual/2020/poster/18491,https://papers.nips.cc/paper_files/paper/2020/file/05e2a0647e260c355dd2b2175edb45b8-Paper.pdf,offline_nips,,"Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in some high-dimensional space, and usually lack the flexibility of being directly applicable to down-stream applications. "
80,mIGCz3ZmmX,Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency,Junhao Chen; Manyi Li; zherong pan; Xifeng Gao; Changhe Tu,2025,ICML 2025,main,Reject,deep_learning->generative_models_and_autoencoders,Manifold analysis; Diffusion model; Image manipuation,0,10.582,0.000,,https://openreview.net/forum?id=mIGCz3ZmmX,,offline_icml,,"Building on the manifold hypothesis, which suggests that generative models learn data distributions residing on low-dimensional manifolds, this paper investigates the time-varying manifold sequence induced by the generation process through the lens of differential equations in diffusion models. Our "
81,rkl_Ch4YwS,A TWO-STAGE FRAMEWORK FOR MATHEMATICAL EXPRESSION RECOGNITION,Jin Zhang; Weipeng Ming; Pengfei Liu,2020,ICLR 2020,main,Reject,,mathematical expressions recognition;seq2seq model,0,10.577,0.000,,https://openreview.net/forum?id=rkl_Ch4YwS,,offline_iclr,,"
Although mathematical expressions (MEs) recognition have achieved great progress, the development of MEs recognition in real scenes is still unsatisfactory. Inspired by the recent work of neutral network, this paper proposes a novel two-stage approach which takes a printed mathematical expression i"
82,JPZoLWYo82,WarriorMath: Empowering Mathematical Reasoning for Large Language Models via Expert Battles,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Mathematical NLP;Data Synthesis,0,10.566,0.000,,https://openreview.net/forum?id=JPZoLWYo82,,offline_iclr,,"Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing methods focus on augmenting datasets through rephrasing or difficulty progression but overlook the specific failure modes o"
83,BJlisySYPS,Modelling the influence of data structure on learning in neural networks,S. Goldt; M. Mézard; F. Krzakala; L. Zdeborová,2020,ICLR 2020,main,Reject,,Neural Networks;Generative models;Synthetic data sets;Generalisation;Stochastic Gradient descent,0,10.556,0.000,,https://openreview.net/forum?id=BJlisySYPS,,offline_iclr,We demonstrate how structure in data sets impacts neural networks and introduce a generative model for synthetic data sets that reproduces this impact.,"The lack of crisp mathematical models that capture the structure of real-world
data sets is a major obstacle to the detailed theoretical understanding of deep
neural networks. Here, we first demonstrate the effect of structured data sets
by experimentally comparing the dynamics and the performance o"
84,kKvEleeIsa,MedCalc-R1: Knowledge-Guided Reward Framework for Medical Mathematical Reasoning,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",medical mathematical reasoning;knowledge-guided reward;complex reasoning;large language model,0,10.528,0.000,,https://openreview.net/forum?id=kKvEleeIsa,,offline_iclr,,"Medical mathematical reasoning is a critical component of clinical decision-making, where accuracy directly affects patient safety and treatment outcomes. However, existing large model approaches, while improving complex reasoning ability, often suffer from knowledge degradation, computational bias,"
85,14278,Manifold denoising by Nonlinear Robust Principal Component Analysis,He Lyu; Ningyu Sha; Shuyang Qin; Ming Yan; Yuying Xie,2019,NIPS 2019,main,Poster,,,0,10.525,0.000,,https://nips.cc/virtual/2019/poster/14278,https://papers.nips.cc/paper_files/paper/2019/file/a76c0abe2b7b1b79e70f0073f43c3b44-Paper.pdf,offline_nips,,This paper extends robust principal component analysis (RPCA) to nonlinear manifolds. Suppose that the observed data matrix is the sum of a sparse component and a component drawn from some low dimensional manifold. Is it possible to separate them by using similar ideas as RPCA? Is there any benefit 
86,fvG6ZHrH0B,Back to the Continuous Attractor,Ábel Ságodi; Guillermo Martín-Sánchez; Piotr A Sokol; Il Memming Park,2024,NIPS 2024,main,Poster,neuroscience_and_cognitive_science,continuous attractors;robustness;fast-slow decomposition;generalization,0,10.481,0.000,,https://neurips.cc/virtual/2024/poster/94178,https://openreview.net/pdf?id=fvG6ZHrH0B,offline_nips,,"Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals.
Unfortunately, continuous attractors suffer from severe structural instability in general---they are destroyed by most infinitesimal change"
87,2NZxmGjDZj,Learning the energy relaxation manifold from unrelaxed structures with RelaxNet,,2026,ICLR 2026,main,Active,"applications to physical sciences (physics, chemistry, biology, etc.)",neural ODEs;energy minimization;trajectory;relaxation;forcefield;optimization,0,10.478,0.000,,https://openreview.net/forum?id=2NZxmGjDZj,,offline_iclr,,"In an effort to bypass computationally expensive density functional theory (DFT) calculations for energy minimization and structure relaxation, rapid progress in the development of machine learning force fields/interatomic potentials (MLFF/MLIPs) and more robust models that adhere to quantum chemist"
88,e4KSeTmjAe,Reservoir Computing with Spatial Filtering and Manifold Learning for fMRI Classification,,2026,ICLR 2026,main,Active,applications to neuroscience & cognitive science,Reservoir Computing;Common Spatial Patterns;UMAP;fMRI;Classification;Interpretability,0,10.475,0.000,,https://openreview.net/forum?id=e4KSeTmjAe,,offline_iclr,,"We introduce a parametric framework that couples discriminative spatial filtering with  reservoir computing to distinguish spatiotemporal structure in resting-state fMRI in two classes. Temporal dependencies are encoded in a reservoir, while supervised spatial filtering on reservoir states isolates "
89,jbafwTkVUn,"Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization",Shiyu Wang; Mariam Avagyan; Yihan Shen; Arnaud Lamy; Tingran Wang,2025,ICML 2025,main,Poster,optimization->everything_else,Manifold Denoising;Learning-to-optimize,0,10.468,0.000,,https://icml.cc/virtual/2025/poster/44296,https://openreview.net/pdf?id=jbafwTkVUn,offline_icml,,"Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in data. Existing denoising methods, however, either rely on loc"
90,Kk08XcQCl2,The data manifold under the microscope,,2026,ICLR 2026,main,Active,learning theory,data manifold;manifold learning;generalization bounds  controlled datasets  deep learning theory,0,10.449,0.000,,https://openreview.net/forum?id=Kk08XcQCl2,,offline_iclr,,"A significant gap exists between theory and practice in deep learning. One example is given by generalization and approximation error bounds, which are often derived for overly simplified models or yield guarantees that are too loose to be informative. Many such bounds rely on the manifold hypothesi"
91,h-UkhDzFFj,Geo-NN: An End-to-End Framework for Geodesic Mean Estimation on the Manifold of Symmetric Positive Definite Matrices,Niharika Shimona D'Souza; Archana Venkataraman,2023,ICLR 2023,main,Withdraw,,Symmetric Postive Definite Manifolds;Geodesic Mean;Matrix Autoencoder,0,10.402,0.000,,https://openreview.net/forum?id=h-UkhDzFFj,,offline_iclr,"We propose an end-to-end deep learning framework, the Geo-NN, to efficiently compute the geodesic mean of a collection of matrices lying on the SPD manifold","The manifold of symmetric positive definite (SPD) matrices plays a key role in many domains, from network science to differential geometry to signal and image processing. However, leveraging the SPD manifold geometry during inference is challenging, as simple operations, such as mean estimation, do "
92,N2wYPMpifA,Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data,Alexander Havrilla; Wenjing Liao,2024,NIPS 2024,main,Poster,learning_theory,scaling laws;LLMs;approximation theory;statistical theory,0,10.383,0.000,,https://neurips.cc/virtual/2024/poster/95466,https://openreview.net/pdf?id=N2wYPMpifA,offline_nips,,"When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the best known example of such scaling laws are for transformer-based large language models (**LLMs**), where networks with b"
93,Guo2XGgxZA,LoRA-S: An Efficient Low Rank Adaptation scheme via Sylvester equation,,2026,ICLR 2026,main,Active,optimization,optimization;LoRA,0,10.373,0.000,,https://openreview.net/forum?id=Guo2XGgxZA,,offline_iclr,,"Numerous studies on low-rank adaptation (LoRA) emerged in recent years, with the aim of accelerating the convergence of the LoRA framework. In this paper, we leverage the horizontal lift theory from differential geometry to establish the general iteration scheme on the quotient manifold  \mathbb{R}\"
94,E5DYpUWsES,"Manifold K-means with $\ell_{2,p}$-Norm Maximization",Fangfang Li; Quanxue Gao; Qianqian Wang; Cheng Deng; Xiaoke Ma,2025,ICLR 2025,main,Withdraw,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Clustering;Manifold Learning;K-means;$\ell_{2;p}$-Norm,0,10.347,0.000,,https://openreview.net/forum?id=E5DYpUWsES,,offline_iclr,,"Although a variety of different methods have emerged in the field of clustering, K-means still occupies an important position, and many advanced clustering methods even rely on the K-means  to achieve effective cluster detection. However, the sensitivity of K-means to the selection of the initial cl"
95,16637,Discrete Probabilistic Inverse Optimal Transport,Wei-Ting Chiu; Pei Wang; Patrick Shafto,2022,ICML 2022,main,Spotlight,,,0,10.309,0.000,,https://icml.cc/virtual/2022/poster/16637,https://proceedings.mlr.press/v162/chiu22b/chiu22b.pdf,offline_icml,,Inverse Optimal Transport (IOT) studies the problem of inferring the underlying cost that gives rise to an observation on coupling two probability measures. Couplings appear as the outcome of matching sets (e.g. dating) and moving distributions (e.g. transportation). Compared to Optimal transport (O
96,-3cHWtrbLYq,Local Identifiability of Deep ReLU Neural Networks: the Theory,Joachim Bona-Pellissier; Francois Malgouyres; Francois Bachoc,2022,NIPS 2022,main,Accept,,Deep Learning;ReLU networks;Conditions of identifiability;Lifting operator,0,10.296,0.000,,https://nips.cc/virtual/2022/poster/53394,https://openreview.net/pdf?id=-3cHWtrbLYq,offline_nips,We characterize theoretically the question of local identifiability for deep ReLU neural networks and we provide numerically testable conditions.,"Is a sample rich enough to determine, at least locally, the parameters of a neural network? To answer this question, we introduce a new local parameterization of a given deep ReLU neural network by fixing the values of some of its weights. This allows us to define local lifting operators whose inver"
97,38fdde6038,Low-rank tensor completion: a Riemannian manifold preconditioning approach,Hiroyuki Kasai; Bamdev Mishra,2016,ICML 2016,main,Poster,,,0,10.252,0.000,,https://proceedings.mlr.press/v48/kasai16.html,http://proceedings.mlr.press/v48/kasai16.pdf,offline_icml,,We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in 
98,r1xHxgrKwr,Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning,Xiaoyan Li; Iluju Kiringa; Tet Yeap; Xiaodan Zhu; Yifeng Li,2020,ICLR 2020,main,Reject,,anomaly detection;disentangled representation learning;manifold learning,0,10.234,0.000,,https://openreview.net/forum?id=r1xHxgrKwr,,offline_iclr,We developed anomaly detection framework based on beta-VAE and t-SNE,"Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representat"
99,lqeVCc9zYq,SMaRt: Improving GANs with Score Matching Regularity,Mengfei Xia; Yujun Shen; Ceyuan Yang; Ran Yi; Wenping Wang,2024,ICML 2024,main,Poster,,,0,10.231,0.000,,https://icml.cc/virtual/2024/poster/33200,https://openreview.net/pdf?id=lqeVCc9zYq,offline_icml,,"Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex. In this work, we revisit the mathematical foundations of GANs, and theoretically reveal that the native adversarial loss for GAN training is insufficient to fix the pro"
100,o3BxOLoxm1,Manifold Preserving Guided Diffusion,Yutong He; Naoki Murata; Chieh-Hsin Lai; Yuhta Takida; Toshimitsu Uesaka,2024,ICLR 2024,main,Poster,generative models,generative model;diffusion model;controllable generation,0,10.221,0.000,,https://iclr.cc/virtual/2024/poster/17837,https://openreview.net/pdf?id=o3BxOLoxm1,offline_iclr,,"Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training. In this paper, we propose Manifold Preserving Guided Diffusion (MPGD), a training-free conditional generation framework that leverages pretrained d"
101,Fj6kQJbHwM9,Manifold Topology Divergence: a Framework for Comparing Data Manifolds.,Serguei Barannikov; Ilya Trofimov; Grigorii Sotnikov; Ekaterina Trimbach; Alexander Korotin,2021,NIPS 2021,main,Poster,,data manifolds;point clouds;persistent homology;topology;generative models;generative adversarial networks;mode-dropping;3D-shapes;time-series,0,10.200,0.000,,https://nips.cc/virtual/2021/poster/27062,https://openreview.net/pdf?id=Fj6kQJbHwM9,offline_nips,We introduce a topology-based domain agnostic methodology for comparing data manifolds.,"We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifol"
102,Cn9Cl08zSS,Language Guided Interpretable Image Recognition via Manifold Alignment,Jiaqi Wang; Pichao WANG; Fan Wang; Liping Jing,2024,ICLR 2024,main,Withdraw,"representation learning for computer vision, audio, language, and other modalities",Explainable AI;Prototypes;Manifold Alignment,0,10.167,0.000,,https://openreview.net/forum?id=Cn9Cl08zSS,,offline_iclr,,"Most works of interpretable neural networks strive for learning the semantics concepts merely from single modal information such as images. However, humans usually learn semantic concepts from multiple modalities and the semantics is encoded by the brain from fused multi-modal information. Inspired "
103,yubwSWol6K,Canonical normalizing flows for manifold learning,Kyriakos Flouris; Ender Konukoglu,2023,NIPS 2023,main,Poster,,manifold learning flows;normalizing flows;optimization;orthogonalization;sparsity;sparse learning;generative modeling;Riemannian manifold;geometry;metric tensor;orthogonal basis,0,10.080,0.000,,https://nips.cc/virtual/2023/poster/69924,https://openreview.net/pdf?id=yubwSWol6K,offline_nips,,"Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is pr"
104,,Manifold Learning Benefits GANs,Yao Ni; Piotr Koniusz; Richard Hartley; Richard Nock,2022,CVPR 2022,main,Poster,,,0,10.040,0.000,,,https://openaccess.thecvf.com/content/CVPR2022/papers/Ni_Manifold_Learning_Benefits_GANs_CVPR_2022_paper.pdf,offline_cvpr,,"In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds, and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are i"
105,11597ce1e1,Information Diffusion Kernels,Guy Lebanon; John D. Lafferty,2002,NIPS 2002,main,Poster,,,0,9.946,0.000,,https://papers.nips.cc/paper_files/paper/2002/hash/5938b4d054136e5d59ada6ec9c295d7a-Abstract.html,https://papers.nips.cc/paper_files/paper/2002/file/5938b4d054136e5d59ada6ec9c295d7a-Paper.pdf,offline_nips,,"A new family of kernels for statistical learning is introduced that ex- ploits the geometric structure of statistical models. Based on the heat equation on the Riemannian manifold defined by the Fisher informa- tion metric, information diffusion kernels generalize the Gaussian kernel of Euclidean spa"
106,XbydvPq92M,Information-Ordered Bottlenecks for Adaptive Dimensionality Reduction,Matthew Ho; Xiaosheng Zhao; Benjamin Dan Wandelt,2024,ICLR 2024,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Deep Learning;Nonlinear Dimension Reduction and Manifold Learning;Neural Networks;Component Analysis (ICA;PCA;CCA;FLDA);Compressed Sensing and Sparse Reconstruction,0,9.912,0.000,,https://openreview.net/forum?id=XbydvPq92M,,offline_iclr,,"We present the information-ordered bottleneck (IOB), a neural layer designed to adaptively compress data into latent variables ordered by likelihood maximization. Without retraining, IOB nodes can be truncated at any bottleneck width, capturing the most crucial information in the first latent variab"
107,2d2f902017,Adaptive Manifold Learning,Jing Wang; Zhenyue Zhang; Hongyuan Zha,2004,NIPS 2004,main,Poster,,,0,9.880,0.000,,https://papers.nips.cc/paper_files/paper/2004/hash/eb0ecdb070a1a0ac46de0cd733d39cf3-Abstract.html,https://papers.nips.cc/paper_files/paper/2004/file/eb0ecdb070a1a0ac46de0cd733d39cf3-Paper.pdf,offline_nips,,"Recently, there have been several advances in the machine learning and           pattern recognition communities for developing manifold learning algo-           rithms to construct nonlinear low-dimensional manifolds from sample           data points embedded in high-dimensional spaces. In this pap"
108,eyE9Fb2AvOT,The Gyro-Structure of Some Matrix Manifolds,Xuan Son Nguyen,2022,NIPS 2022,main,Accept,,manifold learning;representation learning;deep learning;gyrovector spaces,0,9.876,0.000,,https://nips.cc/virtual/2022/poster/53244,https://openreview.net/pdf?id=eyE9Fb2AvOT,offline_nips,This paper studies the gyrovector space structure (gyro-structure) of some matrix manifolds,"In this paper, we study the gyrovector space structure (gyro-structure) of matrix manifolds. Our work is motivated by the success of hyperbolic neural networks (HNNs) that have demonstrated impressive performance in a variety of applications. At the heart of HNNs is the theory of gyrovector spaces t"
109,17120,Flows for simultaneous manifold learning and density estimation,Johann Brehmer; Kyle Cranmer,2020,NIPS 2020,main,Poster,,,0,9.861,0.000,,https://nips.cc/virtual/2020/poster/17120,https://papers.nips.cc/paper_files/paper/2020/file/051928341be67dcba03f0e04104d9047-Paper.pdf,offline_nips,,"We introduce manifold-learning flows (ℳ-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Combining aspects of normalizing flows, GANs, autoencoders, and energy-based models, they have the potential to re"
110,lbasmwFWzH,Riemannian Low-Rank Adaptation for Federated Fine-Tuning of Foundation Models,Zihan Zhou; Yang Zhou; Tianshi Che; Zeru Zhang; Jiaxiang Ren,2025,ICLR 2025,main,Withdraw,"other topics in machine learning (i.e., none of the above)",Rank-adaptive LoRA;Federated Learning;Fine-Tuning;Foundation Models;Riemannian Theory,0,9.857,0.000,,https://openreview.net/forum?id=lbasmwFWzH,,offline_iclr,,"Rank-adaptive low-rank adaptation (LoRA), a parameter-efficient fine-tuning (PEFT) technology, has achieved state-of-the-art performance in fine-tuning foundation models (FM). Directly transplanting the rank-adaptive LoRA methods from centralized learning to federated learning raises two critical is"
111,aDjVZYOZzR,Content Moderation and the Formation of Online Communities: A Theoretical Framework,Cynthia Dwork; Chris Hays; Jon Kleinberg; Manish Raghavan,2024,WWW 2024,main,Oral,,content moderation;online platforms;online communities;social media,0,7.647,0.000,,,,offline_www,,
112,article-28937,A General Theoretical Framework for Learning Smallest Interpretable Models,Sebastian Ordyniak; Giacomo Paesani; Mateusz Rychlicki; Stefan Szeider,2024,AAAI 2024,main,Technical,knowledge representation and reasoning,,0,7.313,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/28937,https://ojs.aaai.org/index.php/AAAI/article/view/28937/29781,offline_aaai,,We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By showing this extension property for decision tree
113,4DJ62eO2T2,CoT-Space: A Theoretical Framework for Internal Slow-Thinking via Reinforcement Learning,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",large language model;reasoning;test-time scaling,0,7.304,0.000,,https://openreview.net/forum?id=4DJ62eO2T2,,offline_iclr,,"Reinforcement Learning (RL) has become a pivotal approach for enhancing the reasoning capabilities of Large Language Models (LLMs). However, a significant theoretical gap persists, as traditional token-level RL frameworks fail to align with the reasoning-level nature of complex, multi-step thought p"
114,ElvhiUFA02,A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning,Chungpa Lee; Jeongheon Oh; Kibok Lee; Jy-yong Sohn,2025,AISTATS 2025,main,Poster,,,0,7.257,0.000,,https://openreview.net/forum?id=ElvhiUFA02,,offline_aistats,,"Supervised contrastive learning (SupCL) has emerged as a prominent approach in representation learning, leveraging both supervised and self-supervised losses. However, achieving an optimal balance between these losses is challenging; failing to do so can lead to class collapse, reducing discriminati"
115,Gp6VU0oJX3,A Causal Theoretical Framework for Open Set Domain Adaptation,Huaming Du; Lei Yuan; Gang Kou; Carl Yang,2025,ICLR 2025,main,Reject,causal reasoning,Causal theory;open set domain adaptation;domain adaptation;empirical risk minimization,0,7.254,0.000,,https://openreview.net/forum?id=Gp6VU0oJX3,,offline_iclr,,"Open Set Domain Adaptation (OSDA) faces two critical challenges: the emergence
of unknown classes in the target domain and changes in observed distributions
across domains. Although numerous studies have proposed advanced algorithms,
recent experimental results demonstrate that the classical Empiric"
116,gscscNNiPN,Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data,Corinna Cortes; Anqi Mao; Mehryar Mohri; Yutao Zhong,2025,ICML 2025,main,Poster,general_machine_learning->supervised_learning,imbalanced data;consistency;margin bounds;learning theory,0,7.224,0.000,,https://icml.cc/virtual/2025/poster/44448,https://openreview.net/pdf?id=gscscNNiPN,offline_icml,,"Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-sensitive techniques, and logistic loss modifications, though popular and often effective, lack solid theoretical foundati"
117,h4hIuid0HY,Modeling SRP-LSH Performance: A Theoretical Framework for Optimizing Approximate Nearest Neighbor Search,,2026,ICLR 2026,main,Active,learning theory,Approximate Nearest Neighbor Search; Locality-Sensitive Hashing; Theoretical Analysis; Parameter Optimization; High-Dimensional Retrieval,0,7.211,0.000,,https://openreview.net/forum?id=h4hIuid0HY,,offline_iclr,,Approximate nearest neighbor (ANN) search in high-dimensional spaces with sign-random-projection locality-sensitive hashing (SRP-LSH) remains challenging due to the lack of principled approaches for configuring its key parameters. We present a theoretical framework that rigorously models SRP-LSH pe
118,paper776,Theoretical Study on Multi-objective Heuristic Search,Shawn Skyler; Shahaf Shperberg; Dor Atzmon; Ariel Felner; Oren Salzman,2024,IJCAI 2024,main,Poster,Search,Search: S: Heuristic search; Search: S: Other; Search: General,0,7.178,0.000,,https://www.ijcai.org/proceedings/2024/776,https://www.ijcai.org/proceedings/2024/0776.pdf,offline_ijcai,,"This paper provides a theoretical study on Multi-Objective Heuristic Search. We first classify states in the state space into must-expand, maybe-expand, and never-expand states and then transfer these definitions to nodes in the search tree. We then formalize a framework that generalizes A* to Multi"
119,P4WnvhVmPV,A Unified Theoretical Framework for Understanding Difficult-to-learn Examples in Contrastive Learning,Yi-Ge Zhang; Jingyi Cui; Qiran Li; Yisen Wang,2025,ICLR 2025,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Machine Learning; Contrastive Learning; Difficult-to-learn Examples,0,7.130,0.000,,https://openreview.net/forum?id=P4WnvhVmPV,,offline_iclr,,"Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However, its learning mechanism is fundamentally different from that of supervised learning. Previous works have shown that diffi"
120,oztUriaGPk,Bernoulli-LoRA: A Theoretical Framework for Randomized Low-Rank Adaptation,,2026,ICLR 2026,main,Active,optimization,Parameter-Efficient Fine-Tuning;Low-Rank Adaptation;Non-convex Optimization;Non-smooth Optimization;Stochastic Optimization;Variance Reduction;Adaptive Stepsizes,0,7.072,0.000,,https://openreview.net/forum?id=oztUriaGPk,,offline_iclr,,"Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large foundational models to specific tasks, particularly as model sizes continue to grow exponentially. Among PEFT methods, Low-Rank Adaptation (LoRA) [Hu et al., 2021] stands out for its effectiveness and simplic"
121,2024.naacl-long.332,Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations,Enze Shi; Lei Ding; Linglong Kong; Bei Jiang,2024,NAACL 2024,main,Long,,,0,7.065,0.000,,https://aclanthology.org/2024.naacl-long.332/,https://aclanthology.org/2024.naacl-long.332.pdf,offline_naacl,,Pre-trained vector representations in natural language processing often inadvertently encode undesirable social biases. Identifying and removing unwanted biased information from vector representation is an evolving and significant challenge. Our study uniquely addresses this issue from the perspecti
122,YzHbFwYmE1,A Theoretical Framework for Rate-Distortion Limits in Learned Image Compression,,2026,ICLR 2026,main,Active,learning theory,Rate-Distortion Limits;Learned Image Compression;Information Theory;Reverse Water-filling;Context Modeling,0,7.034,0.000,,https://openreview.net/forum?id=YzHbFwYmE1,,offline_iclr,,"We present a novel systematic theoretical framework to analyze the rate-distortion (R-D) limits of learned image compression. While recent neural codecs have achieved remarkable empirical results, their distance from the information-theoretic limit remains unclear. Our work addresses this gap by dec"
123,T85ADT8a2y,A Unified Framework for Fair Graph Generation: Theoretical Guarantees and Empirical Advances,Zichong Wang; Zhipeng Yin; Wenbin Zhang,2025,NIPS 2025,main,Poster,social_and_economic_aspects_of_machine_learning,Fairness;Graph Generation;GNN,0,6.984,0.000,,https://openreview.net/forum?id=T85ADT8a2y,,offline_nips,,"Graph generation models play pivotal roles in many real-world applications, from data augmentation to privacy-preserving. Despite their deployment successes, existing approaches often exhibit fairness issues, limiting their adoption in high-risk decision-making applications. Most existing fair graph"
124,DSOTgzeH3w,On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy,,2026,ICLR 2026,main,Active,interpretability and explainable AI,sparse autoencoder;SAE;theoretical understanding,0,6.945,0.000,,https://openreview.net/forum?id=DSOTgzeH3w,,offline_iclr,,"Sparse autoencoders (SAEs) have recently emerged as a powerful tool for interpreting the features learned by large language models (LLMs). By reconstructing features with sparsely activated networks, SAEs aim to recover complex superposed polysemantic features into interpretable monosemantic ones. D"
125,n3ZXEQKRbO,Bridging Debiasing Tasks with Sufficient Projection: A General Theoretical Framework for Vector Representations,Enze Shi; Lei Ding; Linglong Kong; Bei Jiang,2024,ICLR 2024,main,Withdraw,"societal considerations including fairness, safety, privacy",Gender Debias; Vector Representation; NLP; Algorithmic Fairness,0,6.928,0.000,,https://openreview.net/forum?id=n3ZXEQKRbO,,offline_iclr,,Pre-trained vector representations in natural language processing often inadvertently encode undesirable social biases. Identifying and removing unwanted biased information from vector representation is an evolving and significant challenge. Our study uniquely addresses this issue from the perspecti
126,a8uipkMIZN,A Theoretical Framework for Escaping Local Optima in MSE toward Global Convergence,,2026,ICLR 2026,main,Active,optimization,Mean Square Error;Local Optimum;Linear Algebra,0,6.927,0.000,,https://openreview.net/forum?id=a8uipkMIZN,,offline_iclr,,"Deep learning models are trained by minimizing loss functions such as mean squared error (MSE) or cross-entropy, but these objectives are highly non-convex. As a result, optimization often encounters local optima, saddle points, or sharp valleys that hinder convergence and generalization. Although m"
127,DwZD97uHgm,Zero-shot Denoising via Neural Compression: Theoretical and algorithmic framework,Ali Zafari; Xi Chen; Shirin Jalali,2025,NIPS 2025,main,Spotlight,general_machine_learning,compression-based denoising;zero-shot image denoising;neural compression,0,6.887,0.000,,https://openreview.net/forum?id=DwZD97uHgm,,offline_nips,,"Zero-shot denoising aims to denoise observations without access to training samples or clean reference images. This setting is particularly relevant in practical imaging scenarios involving specialized domains such as  medical imaging or biology. In this work, we propose the *Zero-Shot Neural Compre"
128,3LMI8CHDb0g,Reproducibility in Optimization: Theoretical Framework and Limits,Kwangjun Ahn; Prateek Jain; Ziwei Ji; Satyen Kale; Praneeth Netrapalli,2022,NIPS 2022,main,Accept,,reproducibility;first-order optimization;convex optimization;inexact gradient oracles,0,6.875,0.000,,https://nips.cc/virtual/2022/poster/54471,https://openreview.net/pdf?id=3LMI8CHDb0g,offline_nips,We initiate a formal study of reproducibility in optimization by defining a quantitative measure and characterizing the fundamental limits for various settings., We initiate a formal study of reproducibility in optimization. We define a quantitative measure of reproducibility of optimization procedures in the face of noisy or error-prone operations such as inexact or stochastic gradient computations or inexact initialization. We then analyze several convex 
129,VSKV3GykuE,RAC-LoRA: A Theoretical Optimization Framework for Low-Rank Adaptation,Grigory Malinovsky; Umberto Michieli; Hasan Abed Al Kader Hammoud; Taha Ceritli; Hayder Elesedy,2025,ICLR 2025,main,Reject,optimization,LORA;optimization;stochastic optimization;low-rank adaptation,0,6.848,0.000,,https://openreview.net/forum?id=VSKV3GykuE,,offline_iclr,,"Fine-tuning has become a popular approach to adapting large foundational models to specific tasks. As the size of models and datasets grows, parameter-efficient fine-tuning techniques are increasingly important. One of the most widely used methods is Low-Rank Adaptation (LoRA), with adaptation updat"
130,ospGnpuf6L,Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee,Flint Xiaofeng Fan; Yining Ma; Zhongxiang Dai; Wei Jing; Cheston Tan,2021,NIPS 2021,main,Poster,,Reinforcement learning;federated learning;Byzantine-tolerant optimization,0,6.808,0.000,,https://nips.cc/virtual/2021/poster/28144,https://openreview.net/pdf?id=ospGnpuf6L,offline_nips,"This paper provides the theoretical ground to study the sample efficiency of Federated Reinforcement Learning with respect to the number of participating agents, accounting for faulty agents.","The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories. Despite its promising applications, existing works on FRL fail to I) pro"
131,7yJMZwhIC2k,A Theoretical Framework for Inference Learning,Nicholas Alonso; Beren Millidge; Jeffrey Krichmar; Emre Neftci,2022,NIPS 2022,main,Accept,,Predictive Coding;Backpropagation;Synaptic Plasticity;Local Learning;Inference Learning,0,6.807,0.000,,https://nips.cc/virtual/2022/poster/53058,https://openreview.net/pdf?id=7yJMZwhIC2k,offline_nips,"In this paper, we develop a novel theoretical framework for inference learning, a biologically plausible local learning algorithm for deep neural networks.","Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiology. This difficulty has stimulated interest in more biologically plausible alternatives to BP. One such algorithm is the "
132,17555,Functional Regularization for Representation Learning: A Unified Theoretical Perspective,Siddhant Garg; Yingyu Liang,2020,NIPS 2020,main,Poster,,,0,6.785,0.000,,https://nips.cc/virtual/2020/poster/17555,https://papers.nips.cc/paper_files/paper/2020/file/c793b3be8f18731f2a4c627fb3c6c63d-Paper.pdf,offline_nips,,"Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks. While these approaches are widely used in practice and achieve impressive empirical gains, their theoretical understanding largely lags behind. Towards bridging t"
133,gggnCQBT_iE,Connecting Data to Mechanisms with Meta Structual Causal Model,Gong Heyang,2022,ICLR 2022,main,Reject,,meta-SCM;cyclic causal models;sufficient activated mechanisms,0,6.730,0.000,,https://openreview.net/forum?id=gggnCQBT_iE,,offline_iclr,,"Recent years have seen impressive progress in theoretical and algorithmic developments of causal inference across various disciplines in science and engineering. However, there is still some unresolved theoretical problems, especially for cyclic causal relationships. In this article, we propose a me"
134,JrxJUMqqz4,polybasic Speculative Decoding Through a Theoretical Perspective,Ruilin Wang; Huixia Li; Yuexiao Ma; Xiawu Zheng; Fei Chao,2025,ICML 2025,main,Poster,deep_learning->large_language_models,speculative decoding,0,6.697,0.000,,https://icml.cc/virtual/2025/poster/45669,https://openreview.net/pdf?id=JrxJUMqqz4,offline_icml,,"Inference latency stands as a critical bottleneck in the large-scale deployment of Large Language Models (LLMs). Speculative decoding methods have recently shown promise in accelerating inference without compromising the output distribution. However, existing work typically relies on a dualistic dra"
135,yTBXeXdbMf,Provable Reward-Agnostic Preference-Based Reinforcement Learning,Wenhao Zhan; Masatoshi Uehara; Wen Sun; Jason D. Lee,2024,ICLR 2024,main,Spotlight,reinforcement learning,reinforcement learning theory;reward-agnostic learning,0,6.653,0.000,,https://iclr.cc/virtual/2024/poster/17417,https://openreview.net/pdf?id=yTBXeXdbMf,offline_iclr,,"Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoret"
136,DUXG9E8dEO,Theoretical Analysis of Contrastive Learning under Imbalanced Data: From Training Dynamics to a Pruning Solution,,2026,ICLR 2026,main,Active,learning theory,Contrastive learning;Feature learning;Training dynamics;Theoretical analysis,0,6.644,0.000,,https://openreview.net/forum?id=DUXG9E8dEO,,offline_iclr,,"Contrastive learning has emerged as a powerful framework for learning generalizable representations, yet its theoretical understanding remains limited, particularly under imbalanced data distributions that are prevalent in real-world applications. Such an imbalance can degrade representation quality"
137,Hh7x3c0cZl,Theoretical Modeling of Large Language Model Self-Improvement Training Dynamics Through Solver-Verifier Gap,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Training Dynamics;Self-Improvement,0,6.631,0.000,,https://openreview.net/forum?id=Hh7x3c0cZl,,offline_iclr,,"Self-improvement is a significant techniques within the realm of large language model (LLM), aiming to enhance the LLM performance without relying on external data. Despite its significance, generally how LLM performances evolve during the self-improvement process remains underexplored. In this pape"
138,18846,Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations,Zhe Zeng; Paolo Morettin; Fanqi Yan; Antonio Vergari; Guy Van den Broeck,2020,NIPS 2020,main,Spotlight,,,0,6.617,0.000,,https://nips.cc/virtual/2020/poster/18846,https://papers.nips.cc/paper_files/paper/2020/file/85934679f30131d812a8c7475a7d0f74-Paper.pdf,offline_nips,,"Weighted model integration (WMI) is a framework to perform advanced probabilistic inference on hybrid domains, i.e., on distributions over mixed continuous-discrete random variables and in presence of complex logical and arithmetic constraints. In this work, we advance the WMI framework on both the "
139,5EtSvYUU0v,Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime,Yehonatan Avidan; Qianyi Li; Haim Sompolinsky,2024,ICLR 2024,main,Reject,learning theory,Learning dynamics;Neural tangent kernel;Neural network Gaussian process;Infinite width limit;Representational drift;Statistical mechanics,0,6.602,0.000,,https://openreview.net/forum?id=5EtSvYUU0v,,offline_iclr,,"Artificial neural networks (ANNs) have revolutionized machine learning in recent years, but a complete theoretical framework for their learning process is still lacking. Substantial theoretical advances have been achieved for infinitely wide networks. In this regime, two disparate theoretical framew"
140,17648,A Theoretical Framework for Target Propagation,Alexander Meulemans; Francesco Carzaniga; Johan Suykens; João Sacramento; Benjamin F. Grewe,2020,NIPS 2020,main,Spotlight,,,0,6.569,0.000,,https://nips.cc/virtual/2020/poster/17648,https://papers.nips.cc/paper_files/paper/2020/file/e7a425c6ece20cbc9056f98699b53c6f-Paper.pdf,offline_nips,,"The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), no"
141,498e4b69d0,A Theoretical and Practical Framework for Regression and Classification from Truncated Samples,Andrew Ilyas; Emmanouil Zampetakis; Constantinos Daskalakis,2020,AISTATS 2020,main,Poster,,,0,6.543,0.000,,https://proceedings.mlr.press/v108/ilyas20a.html,http://proceedings.mlr.press/v108/ilyas20a/ilyas20a.pdf,offline_aistats,,"Machine learning and statistics are invaluable for extracting insights from data. A key assumption of most methods, however, is that they have access to independent samples from the distribution of relevant data. As such, these methods often perform poorly in the face of {\em biased data} which brea"
142,17441,Mirror Learning: A Unifying Framework of Policy Optimisation,Jakub Grudzien; Christian A Schroeder De Witt; Jakob Foerster,2022,ICML 2022,main,Spotlight,,,0,6.540,0.000,,https://icml.cc/virtual/2022/poster/17441,https://proceedings.mlr.press/v162/grudzien22a/grudzien22a.pdf,offline_icml,,"Modern deep reinforcement learning (RL) algorithms are motivated by either the general policy improvement (GPI) or trust-region learning (TRL) frameworks. However, algorithms that strictly respect these theoretical frameworks have proven unscalable. Surprisingly, the only known scalable algorithms v"
143,eVlcdbIx2O,A Generative Model for Game Theory with Flow Equilibrium,Zhiyu Zhao; David Henry Mguni; Yali Du; Kaiyang Guo; Haifeng Zhang,2024,ICLR 2024,main,Withdraw,"general machine learning (i.e., none of the above)",Generative Model;Variational Inference;Game Theory,0,6.540,0.000,,https://openreview.net/forum?id=eVlcdbIx2O,,offline_iclr,,"In recent years, generative models have emerged as a groundbreaking development in the field of artificial intelligence, transforming various domains such as image synthesis, natural language processing, and data generation. While recent studies have integrated generative models into multi-agent sce"
144,Tbq5fYViJzm,Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters,Takanori Maehara; Hoang NT,2021,NIPS 2021,main,Poster,,graph parameters;Benjamini-Schramm convergence;random sampling;graph learning theory;graph classification;GNN,0,6.539,0.000,,https://nips.cc/virtual/2021/poster/27448,https://openreview.net/pdf?id=Tbq5fYViJzm,offline_nips,A graph parameter is estimable by GNNs+random sampling if and only if it is continuous in randomized Benjamini-Schramm topology.,"Theoretical analyses for graph learning methods often assume a complete observation of the input graph. Such an assumption might not be useful for handling any-size graphs due to the scalability issues in practice. In this work, we develop a theoretical framework for graph classification problems in"
145,article-31996,A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrödinger Equation,Daniel Freedman; Eyal Rozenberg; Alex Bronstein,2025,AAAI 2025,main,Technical,application domains,,0,6.528,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/31996,https://ojs.aaai.org/index.php/AAAI/article/view/31996/34151,offline_aaai,,"A central problem in quantum mechanics involves solving the Electronic Schrödinger Equation for a molecule or material. The Variational Monte Carlo approach to this problem approximates a particular variational objective via sampling, and then optimizes this approximated objective over a chosen para"
146,nFdJSm9dy83,SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients,Feihu Huang; Junyi Li; Heng Huang,2021,NIPS 2021,main,Poster,,Adaptive Gradient;Adam;Universal Framework;Nonconvex Optimization;Deep Learning,0,6.465,0.000,,https://nips.cc/virtual/2021/poster/27440,https://openreview.net/pdf?id=nFdJSm9dy83,offline_nips,,"Adaptive gradient methods have shown excellent performances for solving many machine learning problems. Although multiple adaptive gradient  methods were recently studied, they mainly focus on either empirical or theoretical aspects and also only work for specific problems by using some  specific ad"
147,9FqARW7dwB,Hyper-Connections,Defa Zhu; Hongzhi Huang; Zihao Huang; Yutao Zeng; Yunyao Mao,2025,ICLR 2025,main,Poster,"foundation or frontier models, including LLMs",Network Architecture;Residual Connections;LLMs;Pre-training,0,1.000,0.615,,https://iclr.cc/virtual/2025/poster/30709,https://openreview.net/pdf?id=9FqARW7dwB,offline_iclr,,"We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theo"
148,A5AejTTloS,Value-Alignment via Safe Semantic Manifold-Constrained Latent Diffusion,,2026,ICLR 2026,main,Active,"alignment, fairness, safety, privacy, and societal considerations",value alignment; diffusion model; Manifold-Constrained,0,0.731,0.433,,https://openreview.net/forum?id=A5AejTTloS,,offline_iclr,,"LLM-based detoxification often shifts explicit toxicity into subtler forms: profanities vanish while harm persists through insinuations, stereotypes, microaggressions, and subtly discriminatory framing. We reformulate detoxification from a value-alignment perspective as a multi-principle constrained"
149,xxY8d4rnSb,ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation,Cédric Rommel; Victor Letzelter; Nermin Samet; Renaud Marlet; Matthieu Cord,2024,NIPS 2024,main,Poster,machine_vision,human pose estimation;depth ambiguity;multiple choice learning,0,0.727,0.390,,https://neurips.cc/virtual/2024/poster/93050,https://openreview.net/pdf?id=xxY8d4rnSb,offline_nips,,"We propose ManiPose, a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting. We provide theoretical and empirical evidence that, due to the depth ambiguity inherent to monocular 3D human pose estimation, traditional regression models suffer from pose-topology consistency issue"
150,VMV8gefvq8,MCNC: Manifold-Constrained Reparameterization for Neural Compression,Chayne Thrash; Reed Andreas; Ali Abbasi; Parsa Nooralinejad; Soroush Abbasi Koohpayegani,2025,ICLR 2025,main,Poster,"other topics in machine learning (i.e., none of the above)",Model Compression;LoRA;PEFT;Transformers;ViT,0,0.683,0.399,,https://iclr.cc/virtual/2025/poster/29420,https://openreview.net/pdf?id=VMV8gefvq8,offline_iclr,,"The outstanding performance of large foundational models across diverse tasks,
from computer vision to speech and natural language processing, has significantly
increased their demand. However, storing and transmitting these models poses
significant challenges due to their massive size (e.g., 750GB "
151,UTNZKl5BUc,Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization,seyed amir hossein saberi; Amir Najafi; Amin Behjati; Ala Emrani; Yasaman Zolfimoselo,2024,NIPS 2024,main,Poster,learning_theory,Gradual Domain Adaptation;Distributionally Robust Optimization;Generalization Bound;Error Propagation Characterization,0,0.677,0.380,,https://neurips.cc/virtual/2024/poster/94967,https://openreview.net/pdf?id=UTNZKl5BUc,offline_nips,,"The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $T\ge2$ data distributions $P_1,\ldots,P_T$ undergoing a gradual shift, where each pair of consecutive measures $P_i,P_{i"
152,32385,Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI,Won Jun Kim; Hyungjin Chung; Jaemin Kim; Sangmin Lee; Byeongsu Sim,2025,CVPR 2025,main,Poster,,,0,0.666,0.395,,https://cvpr.thecvf.com/virtual/2025/poster/32385,https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Derivative-Free_Diffusion_Manifold-Constrained_Gradient_for_Unified_XAI_CVPR_2025_paper.pdf,offline_cvpr,,"Gradient-based methods are a prototypical family of ""explainability for AI"" (XAI) techniques, especially for image-based models. However, they (1) require white-box access to models, (2) are vulnerable to adversarial attacks, and (3) produce attributions that lie off the image manifold, leading to e"
153,ECc2td0LCZ,Manifold-Constrained Gaussian Process Inference for One-shot Learning of Unknown Ordinary Differential Equations,,2026,ICLR 2026,main,Active,"applications to physical sciences (physics, chemistry, biology, etc.)",Gaussian Process;ODE learning,0,0.642,0.402,,https://openreview.net/forum?id=ECc2td0LCZ,,offline_iclr,,"Learning unknown ordinary differential equations (ODEs) from a single trajectory of scarce, noisy data is challenging, especially with partial observability. We introduce MAGI-X, an integration-free framework that couples a neural vector field with a Gaussian process prior over trajectories and enfo"
154,E77uvbOTtp,CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models,Hyungjin Chung; Jeongsol Kim; Geon Yeong Park; Hyelin Nam; Jong Chul Ye,2025,ICLR 2025,main,Poster,generative models,Diffusion models;Manifold;Classifier-free guidance,0,0.603,0.368,,https://iclr.cc/virtual/2025/poster/30421,https://openreview.net/pdf?id=E77uvbOTtp,offline_iclr,,"Classifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality output"
155,13817,Escaping from saddle points on Riemannian manifolds,Yue Sun; Nicolas Flammarion; Maryam Fazel,2019,NIPS 2019,main,Poster,,,0,0.458,0.219,,https://nips.cc/virtual/2019/poster/13817,https://papers.nips.cc/paper_files/paper/2019/file/24e01830d213d75deb99c22b9cd91ddd-Paper.pdf,offline_nips,,"We consider minimizing a nonconvex, smooth function $f$ on a Riemannian manifold $\mathcal{M}$. We show that a perturbed version of the gradient descent algorithm converges to a second-order stationary point for this problem (and hence is able to escape saddle points on the manifold). While the unco"
156,569fe8abe2,Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach,Riccardo Bonalli; Abhishek Cauligi; Andrew Bylard; Thomas Lew; Marco Pavone,2019,RSS 2019,main,Poster,,,0,0.385,0.193,,https://www.roboticsproceedings.org/rss15/p78.html,https://www.roboticsproceedings.org/rss15/p78.pdf,offline_rss,,"Sequential Convex Programming (SCP) has recently gained popularity as a tool for trajectory optimization due to its sound theoretical properties and practical performance. Yet, most SCP-based methods for trajectory optimization are restricted to Euclidean settings, which precludes their application "
157,ghhKZ0NaQN,DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration,Hebaixu Wang; Jing Zhang; Haonan Guo; Di Wang; Jiayi Ma,2025,NIPS 2025,main,Poster,deep_learning,Image restoration;diffusion generalist solver;universal posterior sampling;deep learning,0,0.380,0.299,,https://openreview.net/forum?id=ghhKZ0NaQN,,offline_nips,,"Diffusion models have achieved remarkable progress in universal image restoration. However, existing methods perform naive inference in the reverse process, which leads to cumulative errors under limited sampling steps and large step intervals. Moreover, they struggle to balance the commonality of d"
158,14444,Riemannian batch normalization for SPD neural networks,Daniel Brooks; Olivier Schwander; Frederic Barbaresco; Jean-Yves Schneider; Matthieu Cord,2019,NIPS 2019,main,Poster,,,0,0.379,0.186,,https://nips.cc/virtual/2019/poster/14444,https://papers.nips.cc/paper_files/paper/2019/file/6e69ebbfad976d4637bb4b39de261bf7-Paper.pdf,offline_nips,,"Covariance matrices have attracted attention for machine learning applications due
to their capacity to capture interesting structure in the data. The main challenge
is that one needs to take into account the particular geometry of the Riemannian
manifold of symmetric positive definite (SPD) matrice"
159,RAC3ng3TSN,Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees,Steffen Schotthöfer; M. Paul Laiu,2025,ICLR 2025,main,Reject,optimization,Federated Learning;Low-Rank;Model Compression;Efficient Federated Learning,0,0.378,0.298,,https://openreview.net/forum?id=RAC3ng3TSN,,offline_iclr,,We propose a federated dynamical low-rank training (FeDLRT) scheme to reduce client compute and communication costs - two significant performance bottlenecks in horizontal federated learning. Our method builds upon dynamical low-rank splitting schemes for manifold-constrained optimization to create 
160,61jN0L0aoJ,Beyond Minimax: Structure-Aware Learning for Differential Games,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",pursuit-evasion game;calculus of variations;pontryagin's maximum principle,0,0.377,0.319,,https://openreview.net/forum?id=61jN0L0aoJ,,offline_iclr,,"A central challenge in artificial intelligence is to design agents that solve structured engineering problems, such as zero-sum differential games, without handcrafted solutions or expert demonstrations. Differential games capture multi-agent interactions with opposing objectives, where optimal stra"
161,CxUuCydMDU,Diffusion Probabilistic Models for Structured Node Classification,Hyosoon Jang; Seonghyun Park; Sangwoo Mo; Sungsoo Ahn,2023,NIPS 2023,main,Poster,,diffusion model;graph neural network;structured prediction;node classification,0,0.364,0.254,,https://nips.cc/virtual/2023/poster/72405,https://openreview.net/pdf?id=CxUuCydMDU,offline_nips,,"This paper studies structured node classification on graphs, where the predictions should consider dependencies between the node labels. In particular, we focus on solving the problem for partially labeled graphs where it is essential to incorporate the information in the known label for predicting "
162,QQqDBRRslp,Toward a Unified Geometry Understanding : Riemannian Diffusion Framework for Graph Generation and Prediction,Yisen Gao; Xingcheng Fu; Qingyun Sun; Jianxin Li; Xianxian LI,2025,NIPS 2025,main,Poster,deep_learning,Graph Generation;Hyperbolic Graph Learning;Riemannian Manifold;Graph Learning,0,0.335,0.296,,https://openreview.net/forum?id=QQqDBRRslp,,offline_nips,,"Graph diffusion models have made significant progress in learning structured graph data and have demonstrated strong potential for predictive tasks. Existing approaches typically embed node, edge, and graph-level features into a unified latent space, modeling prediction tasks including classificatio"
163,DsEhqQtfAG,Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems,Hyungjin Chung; Suhyeon Lee; Jong Chul Ye,2024,ICLR 2024,main,Poster,generative models,Diffusion models; Inverse problems; Krylov subspace,0,0.333,0.265,,https://iclr.cc/virtual/2024/poster/19121,https://openreview.net/pdf?id=DsEhqQtfAG,offline_iclr,,"Krylov subspace, which is generated by multiplying a given vector by the matrix of a linear transformation and its successive powers,  has been extensively studied in classical optimization literature to design algorithms that converge quickly for large linear inverse problems. For example, the conj"
164,,Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification,Cui-Na Jiao; Ying-Lian Gao; Na Yu; Jin-Xing Liu; Lianyong Qi,2020,IEEE journal of biomedical and health informatics,,,,,46,0.000,0.138,10.1109/JBHI.2020.2975199,https://www.semanticscholar.org/paper/f68b6e6c27a63f965cd6612cfd67acf1b40d3b5b,,semantic_scholar,,"Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the"
165,,Ground states for the planar NLSE with a point defect as minimizers of the constrained energy,R. Adami; F. Boni; R. Carlone; L. Tentarelli,2021,Calculus of Variations and Partial Differential Equations,,,,,32,0.000,0.120,10.1007/s00526-022-02310-8,https://www.semanticscholar.org/paper/a25875f86614ddf77564ca2f0be903da9dd8d583,,semantic_scholar,,"We investigate the ground states for the focusing, subcritical nonlinear Schrödinger equation with a point defect in dimension two, defined as the minimizers of the energy functional at fixed mass. We prove that ground states exist for every positive mass and show a logarithmic singularity at the de"
166,,The non-adiabatic nanoreactor: towards the automated discovery of photochemistry†,Elisa Pieri; Dean Lahana; Alexander M Chang; Cody R Aldaz; K. Thompson,2021,Chemical Science,,,,,24,0.000,0.126,10.1039/d1sc00775k,https://www.semanticscholar.org/paper/5968f7fd9a1e57e9fc713d17a02013297599a842,https://pubs.rsc.org/en/content/articlepdf/2021/sc/d1sc00775k,semantic_scholar,,"The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules "
167,,MoDANet: Multi-Task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation,Van-Sang Doan; Thien Huynh-The; Van-Phuc Hoang; Duy T. Nguyen,2022,IEEE Communications Letters,,,,,22,0.000,0.138,10.1109/lcomm.2021.3132018,https://www.semanticscholar.org/paper/816c248b74fbd8db43a85747dde57beedab2dbaf,,semantic_scholar,,"In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The mul"
168,,SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization,Songtao Lu,2023,Neural Information Processing Systems,,,,,19,0.000,0.162,,https://www.semanticscholar.org/paper/245e67d4acd761b1d5b82bd5279803a48831c1bf,,semantic_scholar,,
169,,ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond,Wilson Jallet; Antoine Bambade; N. Mansard; Justin Carpentier,2022,arXiv.org,,,,,18,0.000,0.148,10.48550/arXiv.2210.02109,https://www.semanticscholar.org/paper/9a59529464edf5dc477afd2dcce76b892b2964f1,http://arxiv.org/pdf/2210.02109,semantic_scholar,,"Mathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability to work on manifolds (such as the classical matrix Lie groups), along with a specific requirement for robustness and spee"
170,,Asymptotic profiles for a nonlinear Schrödinger equation with critical combined powers nonlinearity,Shiwang Ma; Vitaly Moroz,2023,Mathematische Zeitschrift,,,,,11,0.000,0.150,10.1007/s00209-023-03271-0,https://www.semanticscholar.org/paper/9b75f0de537b0eb389208a43392d8fe2424a38cf,https://link.springer.com/content/pdf/10.1007/s00209-023-03271-0.pdf,semantic_scholar,,"We study asymptotic behaviour of positive ground state solutions of the nonlinear Schrödinger equation $$\begin{aligned} -\Delta u+u=u^{2^*-1}+\lambda u^{q-1} \quad \textrm{in}\, {\mathbb {R}}^N,\qquad \qquad \qquad \qquad \qquad {(P_\lambda )} \end{aligned}$$ - Δ u + u = u 2 ∗ - 1 + λ u q - 1 in R "
171,,Nonholonomic and constrained variational mechanics,A. D. Lewis,2020,,,,,,10,0.000,0.092,10.3934/jgm.2020013,https://www.semanticscholar.org/paper/69a2d7a0fccd5b709fe2aeedfad1e303adec446d,https://www.aimsciences.org/article/exportPdf?id=6438a283-c843-455a-9966-141eb920f0c5,semantic_scholar,,"Equations governing mechanical systems with nonholonomic constraints can be developed in two ways: (1) using the physical principles of Newtonian mechanics; (2) using a constrained variational principle. Generally, the two sets of resulting equations are not equivalent. While mechanics arises from t"
172,,Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder,Emiliano Trimarco; Pierandrea Mirino; Daniele Caligiore,2021,Frontiers in Systems Neuroscience,,,,,7,0.000,0.137,10.3389/fnsys.2021.666649,https://www.semanticscholar.org/paper/105dfc9dd495e4e279cc08f6ae88cfa481806c1f,https://doi.org/10.3389/fnsys.2021.666649,semantic_scholar,,Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic
173,,Revealing the hidden structure of disordered materials by parameterizing their local structural manifold,Thomas J. Hardin; Michael Chandross; Rahul Meena; Spencer Fajardo; Dimitris G. Giovanis,2024,Nature Communications,,,,,6,0.000,0.172,10.1038/s41467-024-48449-0,https://www.semanticscholar.org/paper/7ddf9876b5e857d2fbbb010238501dd51550e605,https://www.nature.com/articles/s41467-024-48449-0.pdf,semantic_scholar,,"Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the latt"
174,,Manifold learning for fMRI time-varying functional connectivity,J. Gonzalez-Castillo; Isabel S. Fernandez; K. Lam; D. Handwerker; Francisco Pereira,2023,Frontiers in Human Neuroscience,,,,,6,0.000,0.163,10.3389/fnhum.2023.1134012,https://www.semanticscholar.org/paper/71ae44f01b9201f94779f59aace871f720a77c6d,https://www.frontiersin.org/articles/10.3389/fnhum.2023.1134012/pdf,semantic_scholar,,"Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimen"
175,,Hyper-holomorphic connections on vector bundles on hyper-Kähler manifolds,Francesco Meazzini; Claudio Onorati,2022,Mathematische Zeitschrift,,,,,5,0.000,0.213,10.1007/s00209-022-03176-4,https://www.semanticscholar.org/paper/23584ba7780c416c7064971241be0e1b4fcb2cae,,semantic_scholar,,"We study infinitesimal deformations of autodual and hyper-holomorphic connections on complex vector bundles on hyper-Kähler manifolds of arbitrary dimension. In particular, we describe the DG Lie algebra controlling this deformation problem. Moreover, we prove associative formality for derived endom"
176,,"Quantum Substrate Dynamics (QSD): A Relativistic Field Model of Emergent Mass, Inertia and Gravity",Michael Bush,2025,Preprints.org,,,,,5,0.000,0.182,10.20944/preprints202506.0988.v2,https://openalex.org/W4411392906,https://www.preprints.org/frontend/manuscript/724c52c071a67c93de9b5da3679bd484/download_pub,openalex,,"Quantum Substrate Dynamics (QSD) is a Lorentz-invariant, coherence-based field theory in which mass, gravity, and inertia emerge from phase-stable excitations within a conserved physical substrate. In this framework, mass appears as a coherence-locked phase lattice, inertia arises from reconfigurati"
177,,"No Minima, No Collisions: Combining Modulation and Control Barrier Function Strategies for Feasible Dynamical Collision Avoidance",Yifan Xue; Nadia Figueroa,2025,arXiv.org,,,,,4,0.000,0.192,10.48550/arXiv.2502.14238,https://www.semanticscholar.org/paper/9e252f402a62a45d310efe1391f85e19133e425a,,semantic_scholar,,"As prominent real-time safety-critical reactive control techniques, Control Barrier Function Quadratic Programs (CBF-QPs) work for control affine systems in general but result in local minima in the generated trajectories and consequently cannot ensure convergence to the goals. Contrarily, Modulatio"
178,,Nonlinear Cauchy Elasticity,Arash Yavari; Alain Goriely,2025,Archive for Rational Mechanics and Analysis,,,,,4,0.000,0.186,10.1007/s00205-025-02120-0,https://openalex.org/W4413891066,https://link.springer.com/content/pdf/10.1007/s00205-025-02120-0.pdf,openalex,,"Abstract Most theories and applications of elasticity rely on an energy function that depends on the strains from which the stresses can be derived. This is the traditional setting of Green elasticity, also known as hyper-elasticity. However, in its original form the theory of elasticity does not as"
179,,"Mock Modularity at Work, or Black Holes in a Forest",Sergei Alexandrov,2025,Entropy,,,,,3,0.000,0.181,10.3390/e27070719,https://openalex.org/W4411972976,https://www.mdpi.com/1099-4300/27/7/719/pdf?version=1751469528,openalex,,"Mock modular forms, first invented by Ramanujan, provide a beautiful generalization of the usual modular forms. In recent years, it was found that they capture the generating functions of the number of microstates of BPS black holes appearing in compactifications of string theory with 8 and 16 super"
180,,Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint,Zhigang Li; Weimin Zheng; Honghong Liu; Jingyu Liu; Chang Yan,2024,IEEE transactions on neural systems and rehabilitation engineering,,,,,3,0.000,0.174,10.1109/TNSRE.2024.3355769,https://www.semanticscholar.org/paper/8f0d342861da43a0c039cbb16c33bfab55b54d5b,https://ieeexplore.ieee.org/ielx7/7333/4359219/10403846.pdf,semantic_scholar,,"The functional architecture undergoes alterations during the preclinical phase of Alzheimer’s disease. Consequently, the primary research focus has shifted towards identifying Alzheimer’s disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data."
181,,Clarke Transform and Clarke Coordinates - A New Kid on the Block for State Representation of Continuum Robots,R. Grassmann; J. Burgner-Kahrs,2024,arXiv.org,,,,,3,0.000,0.168,10.48550/arXiv.2409.13826,https://www.semanticscholar.org/paper/b085d64d385554afe5b567875494a232cb6f9f9f,,semantic_scholar,,"For almost all tendon-driven continuum robots, a segment is actuated by three or four tendons constrained by its mechanical design. For both cases, methods to account for the constraints are known. However, for an arbitrary number of tendons, a disentanglement method has yet to be formulated. Motiva"
182,,A Lightweight Certificateless Signcryption Scheme based on HCC for securing Underwater Wireless Sensor Networks (UWSNs),Meenakshi Gupta; Poonam Gera; Bharavi Mishra,2023,International Conference on Security of Information and Networks,,,,,3,0.000,0.159,10.1109/SIN60469.2023.10474770,https://www.semanticscholar.org/paper/8dfa770c6dfc24568c6ae831e0e133a97b3c0c25,,semantic_scholar,,Underwater Wireless Sensor Networks (UWSNs) consist of sensor nodes deployed within bodies of water. Wireless connections and the harsh underwater environment make sensors susceptible to a variety of malevolent attacks and security concerns. The fundamental concern of the UWSN is secure and reliable
183,,Cognitive Computing Continuum: State-of-the-Art Review and ENACT Vision &amp; Approach,Ioanna Angeliki Kapetanidou; Alexandros Nizamis; Efstathios Karanastasis; Gabriel-Mihail Danciu; Clara Isabel Valero López,2025,Journal of Grid Computing,,,,,2,0.000,0.187,10.1007/s10723-025-09810-9,https://openalex.org/W4413129303,https://link.springer.com/content/pdf/10.1007/s10723-025-09810-9.pdf,openalex,,"Abstract The evolution from the Edge-Cloud Continuum to the Cognitive Computing Continuum (CCC) has introduced new challenges which necessitate advanced frameworks that integrate cognitive capabilities to enhance interoperability, adaptability, and resource efficiency. Considering insights from ongo"
184,,"Kalb-Ramond field, black holes and black strings in (2 + 1)D",Meseret Asrat,2025,Journal of High Energy Physics,,,,,2,0.000,0.181,10.1007/jhep08(2025)135,https://openalex.org/W4413471887,https://link.springer.com/content/pdf/10.1007/JHEP08(2025)135.pdf,openalex,,"A bstract New rotating dilaton black hole and black string solutions in three spacetime dimensions are obtained. The solutions are asymptotically flat and, they are exact in classical string theory. The black hole solutions have only a single horizon. Enclosed inside their horizons, they contain a c"
185,,Codepoietic Generation of Meaningful Information in the Evolving Biosphere,Abir U. Igamberdiev,2025,Entropy,,,,,2,0.000,0.181,10.3390/e27070672,https://openalex.org/W4411607412,https://www.mdpi.com/1099-4300/27/7/672/pdf?version=1750738707,openalex,,"Meaningful information represents reality in its potential form, and its actualization increases the system’s negentropy. Biological evolution leads to the expansion of meaningful information by generating new coding systems (codepoiesis). Through this expansion, any evolutionary change obtains func"
186,,Coriolis Factorizations and their Connections to Riemannian Geometry,Patrick M. Wensing; Johannes Englsberger; Jean-Jacques E. Slotine,2023,arXiv.org,,,,,2,0.000,0.158,10.48550/arXiv.2312.14425,https://www.semanticscholar.org/paper/4aeccd0f9f21f06a686422b3ef0c52455ca48a20,,semantic_scholar,,"Many energy-based control strategies for mechanical systems require the choice of a Coriolis factorization satisfying a skew-symmetry property. This paper (a) explores if and when a control designer has flexibility in this choice, (b) develops a canonical choice related to the Christoffel symbols, a"
187,,Hyper-graph regularized subspace clustering with skip connections for band selection of hyperspectral image,Meng Zeng; Bin Ning; Qiong Gu; Chunyang Hu; Shuijia Li,2022,Computer Science and Information Systems,,,,,2,0.000,0.154,10.2298/csis210830005z,https://www.semanticscholar.org/paper/280a90ebc45e900fd837290bd7f868c37dc04f9c,http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142200005Z,semantic_scholar,,"The Hughes phenomenon of Hyperspectral images (HSIs) with the hundreds of
 continuous narrow bands makes the computational cost of HSIs process ing
 high. Band selection is an effective way to solve such a problem and a lot
 of band selection methods have been proposed in recent years. In this paper"
188,,Regularity of CR maps into uniformly pseudo convex hyper surfaces and applications to proper holomorphic maps,Josef Greilhuber; B. Lamel,2022,ANNALI SCUOLA NORMALE SUPERIORE - CLASSE DI SCIENZE,,,,,2,0.000,0.135,10.2422/2036-2145.202105_009,https://www.semanticscholar.org/paper/076fb2e5f9317a8551e59d2beb7a1c0939f083e4,https://arxiv.org/pdf/2302.14016,semantic_scholar,,We study regularity properties of CR maps in positive codimension valued in pseudoconvex manifolds which carry a nontrivial Levi foliation. We introduce an invariant which can be used to deduce that any sufficiently regular CR map from a minimal manifold into such a foliated target is either generic
189,,Cross-Dimensional Mathematics: A Foundation For STP/STA,Daizhan Cheng,2024,,,,,,1,0.000,0.262,10.1007/s11425-024-2528-4,https://www.semanticscholar.org/paper/39270d175ed49f35e036ca21ee61e7490c5d627f,,semantic_scholar,,"A new mathematical structure, called the cross-dimensional mathematics (CDM), is proposed. The CDM considered in this paper consists of three parts: hyper algebra, hyper geometry, and hyper Lie group/Lie algebra. Hyper algebra proposes some new algebraic structures such as hyper group, hyper ring, a"
190,,Manifold Trajectories in Next-Token Prediction: From Replicator Dynamics to Softmax Equilibrium,Christopher R. Lee-Jenkins,2025,arXiv.org,,,,,1,0.000,0.190,10.48550/arXiv.2508.21186,https://www.semanticscholar.org/paper/5b675c8e73259f41c0ed2b9ab4dad68f21111dc2,,semantic_scholar,,"Decoding in large language models is often described as scoring tokens and normalizing with softmax. We give a minimal, self-contained account of this step as a constrained variational principle on the probability simplex. The discrete, normalization-respecting ascent is the classical multiplicative"
191,,Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges,Obaidullah Zaland; Chanh Nguyen; Florian T. Pokorny; Monowar H. Bhuyan,2025,International Conference on Machine Learning and Computing,,,,,1,0.000,0.185,10.1109/ICMLC66258.2025.11280176,https://www.semanticscholar.org/paper/810506e46071c6581f9c2f608e584339fc713926,,semantic_scholar,,"Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training"
192,,A Connection Between Score Matching and Local Intrinsic Dimension,Eric Yeats; Aaron Jacobson; Darryl Hannan; Yiran Jia; Timothy Doster,2025,arXiv.org,,,,,1,0.000,0.185,10.48550/arXiv.2510.12975,https://www.semanticscholar.org/paper/6bcd7f677e1dbfc1463f4c1762e3a278fd2d692f,,semantic_scholar,,"The local intrinsic dimension (LID) of data is a fundamental quantity in signal processing and learning theory, but quantifying the LID of high-dimensional, complex data has been a historically challenging task. Recent works have discovered that diffusion models capture the LID of data through the s"
193,,Research on Time Series Prediction Model of Quantum Long Short Term Memory Network Fusion,Bing Han; Jian Kang; Hongyu Su,2025,Preprints.org,,,,,1,0.000,0.180,10.20944/preprints202508.0647.v1,https://openalex.org/W4413114546,https://www.preprints.org/frontend/manuscript/cd23a06aae90a6a104783f136fdc0cc1/download_pub,openalex,,This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines quantum graph convolutional networks with classical LSTM. The model takes classical time series data as input and achieves classical quantum information conversion through a quantum encoding layer. Multi scale features are
194,,Neural Architecture Search for Hyperspectral Image Classification: A Comprehensive Review and Future Perspectives,Aili Wang; Xinyu Liu; Kang Zhang; Haoran Lv; Haibin Wu,2025,Remote Sensing,,,,,1,0.000,0.180,10.3390/rs17152727,https://openalex.org/W4413037406,https://www.mdpi.com/2072-4292/17/15/2727/pdf?version=1754553071,openalex,,"Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification performance through automatic feature extraction, manual"
195,,Microstates of AdS5 black holes with hypermultiplets,Marina David; Annelien Vekemans,2025,Journal of High Energy Physics,,,,,1,0.000,0.180,10.1007/jhep07(2025)148,https://openalex.org/W4412403714,https://link.springer.com/content/pdf/10.1007/JHEP07(2025)148.pdf,openalex,,"A bstract We construct supersymmetric rotating AdS 5 black holes in 5d $$ \mathcal{N} $$ <mml:math xmlns:mml=""http://www.w3.org/1998/Math/MathML""> <mml:mi>N</mml:mi> </mml:math> = 2 gauged supergravity coupled to two vector multiplets and a universal hypermultiplet, and verify their microscopic coun"
196,,"A Unified 4D Quantum Projection Framework of Space, Time, and Measurement",Mazen Zaino,2025,,,,,,1,0.000,0.180,10.14293/pr2199.001746.v1,https://openalex.org/W4411509857,https://www.scienceopen.com/document_file/6cb529af-1a5b-4d7d-9a3e-114650db0c6c/ScienceOpenPreprint/A%20Unified%204D%20Quantum%20Projection%20Framework.pdf,openalex,,"This paper presents a novel theoretical framework that aims to unify the core principles of quantum mechanics, general relativity, and thermodynamics by introducing an extended spatial geometry incorporating a compactified fourth spatial dimension. The theory proposes that many of the counterintuiti"
197,,"A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects",Guanglin Niu; Bo Li; Yangguang Lin,2025,,,,,,1,0.000,0.180,10.36227/techrxiv.174961563.32605293/v1,https://openalex.org/W4411222678,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.174961563.32605293/v1,openalex,,
198,,CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge,Yuxi Han; Jihe Wang; Danghui Wang,2024,arXiv.org,,,,,1,0.000,0.175,10.48550/arXiv.2405.19033,https://www.semanticscholar.org/paper/f5d6c679fcc0d9149abe51b9bea8905072593290,,semantic_scholar,,"Graph Neural Networks (GNNs) are computationally demanding and inefficient when applied to graph classification tasks in resource-constrained edge scenarios due to their inherent process, involving multiple rounds of forward and backward propagation. As a lightweight alternative, Hyper-Dimensional C"
199,,On the selection of a proper connection in describing the dynamics of constrained mechanical systems,S. Natsiavas; P. Passas; K. Tzaferis,2023,Nonlinear dynamics,,,,,1,0.000,0.166,10.1007/s11071-023-08326-9,https://www.semanticscholar.org/paper/a95c3704bec9d3c60ba0bf06ed6c9cc2d8d10392,https://link.springer.com/content/pdf/10.1007/s11071-023-08326-9.pdf,semantic_scholar,,"This study is focused on a critical issue related to the direct and consistent application of Newton’s law of motion to a special but large class of mechanical systems, involving equality motion constraints. For these systems, it is advantageous to employ the general analytical dynamics framework, w"
200,,An efficient constraint method for solving planning problems under end-effector constraints,Yahao Wang; Zhen Li; Yanghong Li; Erbao Dong,2024,Industrial robot,,,,,1,0.000,0.164,10.1108/ir-10-2023-0251,https://www.semanticscholar.org/paper/332f84439359c8b9b288e2a00ffbcc6721a94a8b,,semantic_scholar,,"
Purpose
In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.


Design/methodology/approach
In this work"
201,,Manifold Learning for fMRI time-varying FC,J. Gonzalez-Castillo; Isabel S. Fernandez; K. Lam; D. Handwerker; Francisco Pereira,2023,bioRxiv,,,,,1,0.000,0.159,10.1101/2023.01.14.523992,https://www.semanticscholar.org/paper/ef84c2d8052b5af43ae95579f7943c5594cf41ed,https://www.biorxiv.org/content/biorxiv/early/2023/01/16/2023.01.14.523992.full.pdf,semantic_scholar,,
202,,Integral formulas for a foliated sub-Riemannian manifold,V. Rovenski,2021,European Journal of Mathematics,,,,,1,0.000,0.117,10.1007/s40879-023-00593-5,https://www.semanticscholar.org/paper/cb380c2885dfb70009c60e498cd130d99c1b8595,,semantic_scholar,,"We apply the notion of foliation to a nonholonomic manifold, which was introduced for the geometric interpretation of constrained systems in mechanics. We prove a series of integral formulas for a foliated sub-Riemannian manifold, that is, a Riemannian manifold equipped with a distribution $${{\math"
203,,Hyper-Laplacian Regularized Low-Rank Collaborative Representation Classification,Shun Xu; Wenwen Shen,2020,International Conference on Advanced Computational Intelligence,,,,,1,0.000,0.110,10.1109/ICACI49185.2020.9177524,https://www.semanticscholar.org/paper/d00180bafb3fffc3ee95496a0d875909379aaf3f,,semantic_scholar,,"Face recognition is an important branch of computer vision. Domestic and foreign scholars have proposed many algorithms to improve the face recognition rate. However, when the training sample and the test sample are exposed to light, occlusion or contamination, the performance of the proposed algori"
204,,mHC: Manifold-Constrained Hyper-Connections,Zhenda Xie; Yixuan Wei; Huanqi Cao; Chenggang Zhao; Chengqi Deng,2025,arXiv,,,,,0,0.000,0.249,,http://arxiv.org/abs/2512.24880v1,https://arxiv.org/pdf/2512.24880v1,arxiv,,"Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundame"
205,,Hyper-Generalized Weakly Symmetric Para-Sasakian Manifolds and Their Geometric Properties,B. Thangjam; M. Devi,2025,BULLETIN OF THE KARAGANDA UNIVERSITY-MATHEMATICS,,,,,0,0.000,0.226,10.31489/2025m2/241-251,https://www.semanticscholar.org/paper/c673f9021fdb127edcebdf60239985331b4b693c,,semantic_scholar,,"This paper examines para-Sasakian manifolds that satisfy a hyper-generalized weakly symmetric curvature condition. The conditions under which such a manifold with a hyper-generalized weakly symmetric curvature condition satisfies the η-Einstein manifold are established. Furthermore, the geometric be"
206,,Generative Bayesian Hyperparameter Tuning,Hedibert Lopes; Nick Polson; Vadim Sokolov,2025,arXiv,,,,,0,0.000,0.222,,http://arxiv.org/abs/2512.20051v1,https://arxiv.org/pdf/2512.20051v1,arxiv,,"\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper-parameter learning can be difficult due"
207,,Randers metrics with compatible linear connections: a coordinate-free approach,M'ark Ol'ah; Csaba Vincze,2025,Journal of Geometry,,,,,0,0.000,0.218,10.1007/s00022-025-00755-8,https://www.semanticscholar.org/paper/ec2c66f5976abfcd2f630903c6e1a735eefa2a97,,semantic_scholar,,A Randers space is a differentiable manifold equipped with a Randers metric. It is the sum of a Riemannian metric and a one-form on the base manifold. The compatibility of a linear connection with the metric means that the parallel transports preserve the Randers norm of tangent vectors. The existen
208,,"Arithmetic monodromy of hyper-K\""ahler varieties over $p$-adic fields",Kazuhiro Ito; Tetsushi Ito; Teruhisa Koshikawa; Teppei Takamatsu; Haitao Zou,2025,,,,,,0,0.000,0.213,,https://www.semanticscholar.org/paper/73641545c3fda2c186b4b41aa3e27f19d7d96bc6,,semantic_scholar,,"In this paper, we study the $p$-adic and $\ell$-adic monodromy operators associated with hyper-K\""ahler varieties over $p$-adic fields, in connection with Looijenga-Lunts-Verbitsky Lie algebras. We investigate a conjectural relation between the nilpotency indices of these monodromy operators on high"
209,,A Single-Loop First-Order Algorithm for Linearly Constrained Bilevel Optimization,Wei Shen; Jiawei Zhang; Minhui Huang; Cong Shen,2025,arXiv.org,,,,,0,0.000,0.203,10.48550/arXiv.2510.24710,https://www.semanticscholar.org/paper/9db6d6af5761c0e98bac91d2cf09af65880ca0e9,,semantic_scholar,,"We study bilevel optimization problems where the lower-level problems are strongly convex and have coupled linear constraints. To overcome the potential non-smoothness of the hyper-objective and the computational challenges associated with the Hessian matrix, we utilize penalty and augmented Lagrang"
210,,Lagrangian Dual Sections: A Topological Perspective on Hidden Convexity,Venkat Chandrasekaran; Timothy Duff; Jose Israel Rodriguez; Kevin Shu,2025,,,,,,0,0.000,0.200,,https://www.semanticscholar.org/paper/702a8cf538d6f51d0b98a89a34a5d1d55b40b3ac,,semantic_scholar,,"Hidden convexity is a powerful idea in optimization: under the right transformations, nonconvex problems that are seemingly intractable can be solved efficiently using convex optimization. We introduce the notion of a Lagrangian dual section of a nonlinear program defined over a topological space, a"
211,,"Hyperk\""ahler structures on leaves of hyper-Lie Poisson manifolds",Dadi Ni; Kaichuan Qi,2025,,,,,,0,0.000,0.200,,https://www.semanticscholar.org/paper/786f152b5c97bc7c34f3dd3bad4a694af13c23d8,,semantic_scholar,,"Due to its rich structure and close connection with gauge theory, hyperk\""ahler manifolds have attracted increasing interest. Using infinite dimensional hyperk\""ahler reduction, Kronheimer proved that certain adjoint orbits of complexified semisimple Lie algebras admits hyperk\""ahler structures. Lat"
212,,On the rigidity of special and exceptional geometries with torsion a closed $3$-form,Georgios Papadopoulos,2025,arXiv,,,,,0,0.000,0.199,,http://arxiv.org/abs/2511.20568v2,https://arxiv.org/pdf/2511.20568v2,arxiv,,"We demonstrate that all Riemannian manifolds $(M, g, H)$ that admit a connection $\hat\nabla$ with torsion a 3-form $H$, which is both closed $d H=0$ and $\hat\nabla$-covariantly constant, are locally isometric to a product $N\times G$, where $G$ is a semisimple group and $N$ is a Riemannian manifol"
213,,Fundamental Limitations of QAOA on Constrained Problems and a Route to Exponential Enhancement,Chinonso Onah; Kristel Michielsen,2025,arXiv,,,,,0,0.000,0.197,,http://arxiv.org/abs/2511.17259v1,https://arxiv.org/pdf/2511.17259v1,arxiv,,"We study fundamental limitations of the generic Quantum Approximate Optimization Algorithm (QAOA) on constrained problems where valid solutions form a low dimensional manifold inside the Boolean hypercube, and we present a provable route to exponential improvements via constraint embedding. Focusing"
214,,"Lie algebroids, quantum Poisson algebroids, and Lie algebroid connections",Satyendra Kumar Mishra; A. Sarkar,2025,,,,,,0,0.000,0.196,,https://www.semanticscholar.org/paper/4105091889345f991b2875d21437a69e73fd6b4e,,semantic_scholar,,"In this paper, we consider Lie algebroids over commutative ringed spaces. Lie algebroids over ringed spaces unify the existing notion of Lie algebroids over smooth manifolds, complex manifolds, analytic spaces, algebraic varieties, and schemes. We show that the universal enveloping algebroid of a Li"
215,,Trajectory Optimization by Successive Pseudospectral Convexification on Riemannian Manifolds,Tatsuya Narumi; Shin-ichiro Sakai,2025,arXiv,,,,,0,0.000,0.196,,http://arxiv.org/abs/2512.09551v1,https://arxiv.org/pdf/2512.09551v1,arxiv,,"This paper proposes an intrinsic pseudospectral convexification framework for optimal control problems with manifold constraints. While successive pseudospectral convexification combines spectral collocation with successive convexification, classical pseudospectral methods are not geometry-consisten"
216,,Cech - de Rham Chern character on the stack of holomorphic vector bundles,Cheyne Glass; T. Tradler; M. Zeinalian,2025,,,,,,0,0.000,0.196,,https://www.semanticscholar.org/paper/1231982e3a59d14420806f9e0aa5b6d34d2537d5,,semantic_scholar,,We provide a formula for the Chern character of a holomorphic vector bundle in the hyper-cohomology of the de Rham complex of holomorphic sheaves on a complex manifold. This Chern character can be thought of as a completion of the Chern character in Hodge cohomology obtained as the trace of the expo
217,,The algebraic square of an irreducible complex spinor,Alejandro Gil-Garc'ia; C. Shahbazi,2025,,,,,,0,0.000,0.194,,https://www.semanticscholar.org/paper/7a97a602f138d4259eac9619fc1948877dca3b10,,semantic_scholar,,"We characterize, in every dimension and signature, the algebraic squares of an irreducible complex spinor as a pair of exterior forms satisfying a prescribed system of algebraic relations that we present in terms of the geometric product of the underlying quadratic vector space. As a result, we obta"
218,,Quotient Manifold Optimization for Spectral Compressed Sensing,Wenlong Wang; Wen Huang; Zai Yang,2025,arXiv,,,,,0,0.000,0.194,,http://arxiv.org/abs/2511.19108v1,https://arxiv.org/pdf/2511.19108v1,arxiv,,"Spectral compressed sensing involves reconstructing a spectral-sparse signal from a subset of uniformly spaced samples, with applications in radar imaging and wireless channel estimation. By fully exploiting the signal structures, this problem is formulated as a rank-constrained semidefinite program"
219,,"Signatures of real-space geometry, topology, and metric tensor in quantum transport in periodically corrugated spaces",Benjamin Schwager; Theresa Appel; Jamal Berakdar,2025,arXiv,,,,,0,0.000,0.194,,http://arxiv.org/abs/2512.16846v1,https://arxiv.org/pdf/2512.16846v1,arxiv,,The motion of a quantum particle constrained to a two-dimensional non-compact Riemannian manifold with non-trivial metric can be described by a flat-space Schroedinger-type equation at the cost of introducing local mass and metric and geometry-induced effective potential with no classical counterpar
220,,"Secrets of the Goo: The genome assembly of the Pacific banana slug, Ariolimax columbianus",Max Genetti; Merly Escalona; Cade Mirchandani; Jonas Oppenheimer; E. Beraut,2025,Journal of Heredity,,,,,0,0.000,0.193,10.1093/jhered/esaf002,https://www.semanticscholar.org/paper/29dea4859e36ffe56831ffd1850a48666ae0b38e,,semantic_scholar,,"Abstract The Pacific banana slug, Ariolimax columbianus, is endemic to the forests of the Pacific Northern West. Found throughout the coastal foothills and mountains of California, the hermaphroditic molluscs Ariolimax spp. are niche-constrained, hyper-localized, and phenotypically diverse. The evol"
221,,Physics-Constrained Neural Dynamics: A Unified Manifold Framework for Large-Scale Power Flow Computation,Xuezhi Liu,2025,arXiv,,,,,0,0.000,0.193,,http://arxiv.org/abs/2512.01207v1,https://arxiv.org/pdf/2512.01207v1,arxiv,,"Power flow analysis is a fundamental tool for power system analysis, planning, and operational control. Traditional Newton-Raphson methods suffer from limitations such as initial value sensitivity and low efficiency in batch computation, while existing deep learning-based power flow solvers mostly r"
222,,Semantic Geometry for policy-constrained interpretation,Nikit Phadke,2025,arXiv,,,,,0,0.000,0.193,,http://arxiv.org/abs/2512.14731v1,https://arxiv.org/pdf/2512.14731v1,arxiv,,"We present a geometric framework for policy-constrained semantic interpretation that provably prevents hallucinated commitments in high-stakes domains. Semantic meaning is represented as direction on a unit sphere, evidence is modeled as sets of witness vectors, and admissible interpretations corres"
223,,Quantumness via Discrete Structures,Ravi Kunjwal,2025,arXiv,,,,,0,0.000,0.193,,http://arxiv.org/abs/2512.10063v2,https://arxiv.org/pdf/2512.10063v2,arxiv,,"Quantum theory departs from classical probabilistic theories in foundational ways. These departures--termed quantumness here--power quantum information and computation. This thesis charts the role of discrete structures in assessing quantumness, synthesizing elements of my postdoctoral research thro"
224,,Gradient-descent methods for quantum detector tomography,Amanuel Anteneh; Olivier Pfister,2025,arXiv,,,,,0,0.000,0.193,,http://arxiv.org/abs/2511.14579v1,https://arxiv.org/pdf/2511.14579v1,arxiv,,We present a technique for performing quantum detector tomography (QDT) of phase insensitive quantum detectors using gradient descent-based optimization to learn the positive operator-valued measure (POVM) that best describes the data collected using the detector under study. We numerically benchmar
225,,AL-Net: Adaptive Learning for Enhanced Cell Nucleus Segmentation in Pathological Images,Zhuping Chen; Sheng-Lung Peng; Rui Yang; Ming Zhao; Chaolin Zhang,2025,Electronics,,,,,0,0.000,0.193,10.3390/electronics14173507,https://www.semanticscholar.org/paper/85fa9539b6139e5c61a9364cb288675c0ae7e542,,semantic_scholar,,"Precise segmentation of cell nuclei in pathological images is the foundation of cancer diagnosis and quantitative analysis, but blurred boundaries, scale variability, and staining differences have long constrained its reliability. To address this, this paper proposes AL-Net—an adaptive learning netw"
226,,Necessary and sufficient conditions for high dimensional Central Limit Theorem under moment conditions,Debraj Das; Soumendra Lahiri,2025,arXiv,,,,,0,0.000,0.192,,http://arxiv.org/abs/2512.22312v1,https://arxiv.org/pdf/2512.22312v1,arxiv,,"High dimensional central limit theorems (the CLTs) have been extensively studied in recent years under a variety of sufficient moment conditions connecting the dimension growth rate with the tail decay rate. In this article, we investigate whether the existing moment conditions are also necessary un"
227,,ManifoldFormer: Geometric Deep Learning for Neural Dynamics on Riemannian Manifolds,Yihang Fu; Lifang He; Qingyu Chen,2025,arXiv,,,,,0,0.000,0.192,,http://arxiv.org/abs/2511.16828v1,https://arxiv.org/pdf/2511.16828v1,arxiv,,"Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model assumptions and neural geometry lim"
228,,Local Path Optimization in The Latent Space Using Learned Distance Gradient,Jiawei Zhang; Chengchao Bai; Wei Pan; Tianhang Liu; Jifeng Guo,2025,arXiv,,,,,0,0.000,0.191,10.1109/IROS60139.2025.11247535,http://arxiv.org/abs/2512.24272v1,https://arxiv.org/pdf/2512.24272v1,arxiv,,"Constrained motion planning is a common but challenging problem in robotic manipulation. In recent years, data-driven constrained motion planning algorithms have shown impressive planning speed and success rate. Among them, the latent motion method based on manifold approximation is the most efficie"
229,,Guided Path Sampling: Steering Diffusion Models Back on Track with Principled Path Guidance,Haosen Li; Wenshuo Chen; Shaofeng Liang; Lei Wang; Haozhe Jia,2025,arXiv,,,,,0,0.000,0.190,,http://arxiv.org/abs/2512.22881v1,https://arxiv.org/pdf/2512.22881v1,arxiv,,"Iterative refinement methods based on a denoising-inversion cycle are powerful tools for enhancing the quality and control of diffusion models. However, their effectiveness is critically limited when combined with standard Classifier-Free Guidance (CFG). We identify a fundamental limitation: CFG's e"
230,,On Weinstein domains in symplectic manifolds,Thomas E. Mark; Bülent Tosun,2025,arXiv,,,,,0,0.000,0.190,,http://arxiv.org/abs/2512.04278v1,https://arxiv.org/pdf/2512.04278v1,arxiv,,"We prove that a Weinstein domain symplectically embedded in a closed symplectic manifold always admits symplectic hypersurfaces in its complement, possibly after a deformation. As a consequence, we obtain an obstruction for a closed 3-dimensional manifold to arise as the boundary of a Weinstein doma"
231,,Deep Manifold Part 2: Neural Network Mathematics,Max Y. Ma; Gen-Hua Shi,2025,arXiv,,,,,0,0.000,0.189,,http://arxiv.org/abs/2512.06563v1,https://arxiv.org/pdf/2512.06563v1,arxiv,,"This work develops the global equations of neural networks through stacked piecewise manifolds, fixed-point theory, and boundary-conditioned iteration. Once fixed coordinates and operators are removed, a neural network appears as a learnable numerical computation shaped by manifold complexity, high-"
232,,DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraints,Rahul Golder; Bimol Nath Roy; M. M. Faruque Hasan,2025,arXiv,,,,,0,0.000,0.188,,http://arxiv.org/abs/2512.05881v1,https://arxiv.org/pdf/2512.05881v1,arxiv,,"Traditional physics-informed neural networks (PINNs) do not always satisfy physics based constraints, especially when the constraints include differential operators. Rather, they minimize the constraint violations in a soft way. Strict satisfaction of differential-algebraic equations (DAEs) to embed"
233,,"Synergizing Monetization, Orchestration, and Semantics in Computing Continuum",Chinmaya Kumar Dehury; Lauri Lovén; Praveen Kumar Donta; Ilir Murturi; Schahram Dustdar,2025,arXiv,,,,,0,0.000,0.188,,http://arxiv.org/abs/2512.08288v1,https://arxiv.org/pdf/2512.08288v1,arxiv,,"Industry demands are growing for hyper-distributed applications that span from the cloud to the edge in domains such as smart manufacturing, transportation, and agriculture. Yet today's solutions struggle to meet these demands due to inherent limitations in scalability, interoperability, and trust. "
234,,Learning Degenerate Manifolds of Frustrated Magnets with Boltzmann Machines,Jackson C. Glass; Gia-Wei Chern,2025,arXiv,,,,,0,0.000,0.188,,http://arxiv.org/abs/2511.19879v1,https://arxiv.org/pdf/2511.19879v1,arxiv,,"We show that Restricted Boltzmann Machines (RBMs) provide a flexible generative framework for modeling spin configurations in disordered yet strongly correlated phases of frustrated magnets. As a benchmark, we first demonstrate that an RBM can learn the zero-temperature ground-state manifold of the "
235,,GrOMP: Grasped Object Manifold Projection for Multimodal Imitation Learning of Manipulation,William van den Bogert; Gregory Linkowski; Nima Fazeli,2025,arXiv,,,,,0,0.000,0.188,,http://arxiv.org/abs/2512.03347v2,https://arxiv.org/pdf/2512.03347v2,arxiv,,"Imitation Learning (IL) holds great potential for learning repetitive manipulation tasks, such as those in industrial assembly. However, its effectiveness is often limited by insufficient trajectory precision due to compounding errors. In this paper, we introduce Grasped Object Manifold Projection ("
236,,Time integration of quantized tensor trains using the interpolative dynamical low-rank approximation,Erika Ye; Chao Yang,2025,arXiv,,,,,0,0.000,0.187,,http://arxiv.org/abs/2512.15703v1,https://arxiv.org/pdf/2512.15703v1,arxiv,,"Quantized tensor trains (QTTs) are a low-rank and multiscale framework that allows for efficient approximation and manipulation of multi-dimensional, high resolution data. One area of active research is their use in numerical simulation of hyperbolic systems such as the Navier-Stokes equations and t"
237,,Attention Is Not What You Need,Zhang Chong,2025,arXiv,,,,,0,0.000,0.187,,http://arxiv.org/abs/2512.19428v1,https://arxiv.org/pdf/2512.19428v1,arxiv,,We revisit a basic question in sequence modeling: is explicit self-attention actually necessary for strong performance and reasoning? We argue that standard multi-head attention is best seen as a form of tensor lifting: hidden vectors are mapped into a high-dimensional space of pairwise interactions
238,,Hybrid twinning using PBDW and DeepONet for the effective state estimation and prediction on partially known systems,Stiven Briand Massala; Ludovic Chamoin; Massimo Picca Ciamarra,2025,arXiv,,,,,0,0.000,0.187,,http://arxiv.org/abs/2512.11834v1,https://arxiv.org/pdf/2512.11834v1,arxiv,,"The accurate estimation of the state of complex uncertain physical systems requires reconciling theoretical models, with inherent imperfections, with noisy experimental data. In this work, we propose an effective hybrid approach that combines physics-based modeling with data-driven learning to enhan"
239,,Differentiable Inverse Modeling with Physics-Constrained Latent Diffusion for Heterogeneous Subsurface Parameter Fields,Zihan Lin; QiZhi He,2025,arXiv,,,,,0,0.000,0.187,,http://arxiv.org/abs/2512.22421v1,https://arxiv.org/pdf/2512.22421v1,arxiv,,We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknow
240,,An Empirical Study of Sampling Hyperparameters in Diffusion-Based Super-Resolution,Yudhistira Arief Wibowo,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2512.17675v1,https://arxiv.org/pdf/2512.17675v1,arxiv,,"Diffusion models have shown strong potential for solving inverse problems such as single-image super-resolution, where a high-resolution image is recovered from a low-resolution observation using a pretrained unconditional prior. Conditioning methods, including Diffusion Posterior Sampling (DPS) and"
241,,Situationally Sensitive Path Planning,Paul M. Torrens; Ryan Kim; Kaishuu Shinozaki-Conefrey,2025,Algorithms,,,,,0,0.000,0.186,10.3390/a18070388,https://openalex.org/W4411690641,https://www.mdpi.com/1999-4893/18/7/388/pdf?version=1751024551,openalex,,"We examine how site-based path planning algorithms for enclosed spaces can be enhanced with situational detail. Addressing this question has led to value propositions in facility design, where there is often a call to match, map, and merge infrastructure considerations and configurations with potent"
242,,Dark Matter Induced Nucleon Decay Through the Neutron Portal,Nicole F. Bell; Peter Cox; Jayden L. Newstead; Michael B. G. Verde,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2511.18722v1,https://arxiv.org/pdf/2511.18722v1,arxiv,,"The neutron portal operator provides a theoretically motivated connection between the visible and dark sectors and features in several well-studied asymmetric dark matter models. This operator leads to dark matter induced nucleon decays that mimic the experimental signature of ""ordinary"" nucleon dec"
243,,Secure Analog Beamforming for Multi-user MISO Systems with Movable Antennas,Weijie Xiong; Jingran Lin; Kai Zhong; Liu Yang; Hongli Liu,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2511.19360v1,https://arxiv.org/pdf/2511.19360v1,arxiv,,"Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamformi"
244,,Chiral Magnetic Effect induced Spectator Process for Leptogenesis,Wei Chao,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2511.13051v1,https://arxiv.org/pdf/2511.13051v1,arxiv,,"Conventional Leptogenesis mechanism, which provides compelling explanation to the origin of the baryon asymmetry of the universe (BAU), assumes the absence of hypermagnetic field in the early universe, thereby disregard the implications of hyper gauge field helicity, that have been thoroughly studie"
245,,Bhargava Cube--Inspired Quadratic Regularization for Structured Neural Embeddings,S Sairam; Prateek P Kulkarni,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2512.11392v1,https://arxiv.org/pdf/2512.11392v1,arxiv,,We present a novel approach to neural representation learning that incorporates algebraic constraints inspired by Bhargava cubes from number theory. Traditional deep learning methods learn representations in unstructured latent spaces lacking interpretability and mathematical consistency. Our framew
246,,"Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission",Junlin Chang; Yubo Han; Hnag Yue; John S Thompson; Rongke Liu,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2512.03819v1,https://arxiv.org/pdf/2512.03819v1,arxiv,,"The widespread adoption of depth sensors has substantially lowered the barrier to point-cloud acquisition. This letter proposes a semantic wireless transmission framework for three dimension (3D) point clouds built on Deep Joint Source - Channel Coding (DeepJSCC). Instead of sending raw features, th"
247,,Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade,Letian Yi; Tingpeng Zhang; Mingyuan Zhou; Guannan Wang; Quanke Su,2025,arXiv,,,,,0,0.000,0.186,,http://arxiv.org/abs/2512.01572v1,https://arxiv.org/pdf/2512.01572v1,arxiv,,"Reconstructing full fields from extremely sparse and random measurements is a longstanding ill-posed inverse problem. A powerful framework for addressing such challenges is hierarchical probabilistic modeling, where uncertainty is represented by intermediate variables and resolved through marginaliz"
248,,Breaking Symmetry-Induced Degeneracy in Multi-Agent Ergodic Coverage via Stochastic Spectral Control,Kooktae Lee; Julian Martinez,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2512.23158v1,https://arxiv.org/pdf/2512.23158v1,arxiv,,"Multi-agent ergodic coverage via Spectral Multiscale Coverage (SMC) provides a principled framework for driving a team of agents so that their collective time-averaged trajectories match a prescribed spatial distribution. While classical SMC has demonstrated empirical success, it can suffer from gra"
249,,Complexity guarantees and polling strategies for Riemannian direct-search methods,Bastien Cavarretta; Florentin Goyens; Clément W. Royer; Florian Yger,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2511.15360v1,https://arxiv.org/pdf/2511.15360v1,arxiv,,"Direct-search algorithms are derivative-free optimization techniques that operate by polling the variable space along specific directions forming positive spanning sets (PSSs). When the problem variables are constrained to lie on a Riemannian manifold, polling must be performed along tangent directi"
250,,Neural Network Optimal Power Flow via Energy Gradient Flow and Unified Dynamics,Xuezhi Liu,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2512.01219v1,https://arxiv.org/pdf/2512.01219v1,arxiv,,"Optimal Power Flow (OPF) is a core optimization problem in power system operation and planning, aiming to minimize generation costs while satisfying physical constraints such as power flow equations, generator limits, and voltage limits. Traditional OPF solving methods typically employ iterative opt"
251,,Geometry and quantum brachistochrone analysis of multiple entangled spin-1/2 particles under all-range Ising interaction,B. Amghar; M. Yachi; M. Amghar; M. Almousa; A. A. Abd El-Latif,2025,arXiv,,,,,0,0.000,0.185,10.1038/s41598-025-32484-y,http://arxiv.org/abs/2512.21400v1,https://arxiv.org/pdf/2512.21400v1,arxiv,,"We present a unified geometric and dynamical framework for a physical system consisting of $n$ spin-$1/2$ particles with all-range Ising interaction. Using the Fubini-Study formalism, we derive the metric tensor of the associated quantum state manifold and compute the corresponding Riemann curvature"
252,,The Homological Brain: Parity Principle and Amortized Inference,Xin Li,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2512.10976v1,https://arxiv.org/pdf/2512.10976v1,arxiv,,"Biological intelligence emerges from substrates that are slow, noisy, and energetically constrained, yet it performs rapid and coherent inference in open-ended environments. Classical computational theories, built around vector-space transformations and instantaneous error minimization, struggle to "
253,,Training-Free Diffusion Priors for Text-to-Image Generation via Optimization-based Visual Inversion,Samuele Dell'Erba; Andrew D. Bagdanov,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2511.20821v3,https://arxiv.org/pdf/2511.20821v3,arxiv,,"Diffusion models have established the state-of-the-art in text-to-image generation, but their performance often relies on a diffusion prior network to translate text embeddings into the visual manifold for easier decoding. These priors are computationally expensive and require extensive training on "
254,,Connection Between Dwarf Galaxies and Globular Clusters: Insights from the Perseus Cluster Using Subaru Imaging and Keck Spectroscopy,Yimeng Tang; Aaron J. Romanowsky; Song Huang; Nobuhiro Okabe; Jean P. Brodie,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2512.11070v1,https://arxiv.org/pdf/2512.11070v1,arxiv,,"We present a systematic study of 189 dwarf galaxies and their globular cluster (GC) systems in the Perseus cluster, based on deep Subaru Hyper Suprime-Cam imaging and Keck spectroscopy, supplemented by literature data. This constitutes the largest sample of dwarfs in a single galaxy cluster to date "
255,,Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model,Jing He; Haodong Li; Mingzhi Sheng; Ying-Cong Chen,2025,arXiv,,,,,0,0.000,0.185,,http://arxiv.org/abs/2512.01030v2,https://arxiv.org/pdf/2512.01030v2,arxiv,,"Recovering pixel-wise geometric properties from a single image is fundamentally ill-posed due to appearance ambiguity and non-injective mappings between 2D observations and 3D structures. While discriminative regression models achieve strong performance through large-scale supervision, their success"
256,,Transversal Gates in Nonadditive Quantum Codes,Chao Zhang; Zipeng Wu; Shilin Huang; Bei Zeng,2025,,,,,,0,0.000,0.184,,https://www.semanticscholar.org/paper/858abd26e4f99dda2468663d810267fde539c457,,semantic_scholar,,"Transversal gates play a crucial role in suppressing error propagation in fault-tolerant quantum computation, yet they are intrinsically constrained: any nontrivial code encoding a single logical qubit admits only a finite subgroup of $\mathrm{SU}(2)$ as its transversal operations. We introduce a sy"
257,,Geometric Control of Mechanical Systems with Symmetries Based on Sliding Modes,Eduardo Espíndola; Yu Tang,2025,arXiv.org,,,,,0,0.000,0.184,10.48550/arXiv.2509.01985,https://www.semanticscholar.org/paper/358576ff413689af42082670164c424f7898735d,,semantic_scholar,,"In this paper, we propose a framework for designing sliding mode controllers for a class of mechanical systems with symmetry, both unconstrained and constrained, that evolve on principal fiber bundles. Control laws are developed based on the reduced motion equations by exploring symmetries, leading "
258,,Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics,Additi Pandey; Liang Wei; Hessam Babaee; George Em Karniadakis,2025,arXiv,,,,,0,0.000,0.184,,http://arxiv.org/abs/2512.14471v1,https://arxiv.org/pdf/2512.14471v1,arxiv,,"Accurate chemical kinetics modeling is essential for combustion simulations, as it governs the evolution of complex reaction pathways and thermochemical states. In this work, we introduce Kinetic-Mamba, a Mamba-based neural operator framework that integrates the expressive power of neural operators "
259,,Pressure-Tuned Metamagnetism and Emergent Three-Body Interactions in CsFeCl$_3$,K. Nihongi; T. Kida; Y. Narumi; Y. Etoh; D. Yamamoto,2025,arXiv,,,,,0,0.000,0.184,,http://arxiv.org/abs/2512.21682v1,https://arxiv.org/pdf/2512.21682v1,arxiv,,We present a combined experimental and theoretical study of the triangular-lattice quantum antiferromagnet CsFeCl$_3$ under high magnetic fields and high pressure. Pulsed-field magnetization for the magnetic field along the symmetric $c$ direction at ambient pressure reveals a magnetization process 
260,,Quantum measurement tomography with mini-batch stochastic gradient descent,Akshay Gaikwad; Manuel Sebastian Torres; Anton Frisk Kockum,2025,arXiv,,,,,0,0.000,0.184,,http://arxiv.org/abs/2511.15682v1,https://arxiv.org/pdf/2511.15682v1,arxiv,,Drawing inspiration from gradient-descent methods developed for data processing in quantum state tomography [\href{https://iopscience.iop.org/article/10.1088/2058-9565/ae0baa}{Quantum Sci.~Technol.~\textbf{10} 045055 (2025)}] and quantum process tomography [\href{https://journals.aps.org/prl/abstrac
261,,Locality Preserving Markovian Transition for Instance Retrieval,Jifei Luo; Wenzheng Wu; Hantao Yao; Lu Yu,2025,arXiv (Cornell University),,,,,0,0.000,0.183,10.48550/arxiv.2506.05196,https://openalex.org/W4416138634,https://arxiv.org/pdf/2506.05196,openalex,,"Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over several steps away from the source, reducing discriminative power beyond local regions. To address this issue, we introdu"
262,,The Geometry of Persona: Disentangling Personality from Reasoning in Large Language Models,Zhixiang Wang,2025,arXiv,,,,,0,0.000,0.183,,http://arxiv.org/abs/2512.07092v1,https://arxiv.org/pdf/2512.07092v1,arxiv,,"Background: The deployment of personalized Large Language Models (LLMs) is currently constrained by the stability-plasticity dilemma. Prevailing alignment methods, such as Supervised Fine-Tuning (SFT), rely on stochastic weight updates that often incur an ""alignment tax"" -- degrading general reasoni"
263,,UniAct: Unified Motion Generation and Action Streaming for Humanoid Robots,Nan Jiang; Zimo He; Wanhe Yu; Lexi Pang; Yunhao Li,2025,arXiv,,,,,0,0.000,0.183,,http://arxiv.org/abs/2512.24321v1,https://arxiv.org/pdf/2512.24321v1,arxiv,,"A long-standing objective in humanoid robotics is the realization of versatile agents capable of following diverse multimodal instructions with human-level flexibility. Despite advances in humanoid control, bridging high-level multimodal perception with whole-body execution remains a significant bot"
264,,The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence,Pradeep Singh; Mudasani Rushikesh; Bezawada Sri Sai Anurag; Balasubramanian Raman,2025,arXiv,,,,,0,0.000,0.182,,http://arxiv.org/abs/2512.16515v2,https://arxiv.org/pdf/2512.16515v2,arxiv,,"We develop a unified, dynamical-systems narrative of the universe that traces a continuous chain of structure formation from the Big Bang to contemporary human societies and their artificial learning systems. Rather than treating cosmology, astrophysics, geophysics, biology, cognition, and machine i"
265,,3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization,Yuze Hao; Linchao Zhu; Yi Yang,2025,arXiv,,,,,0,0.000,0.182,,http://arxiv.org/abs/2512.08987v1,https://arxiv.org/pdf/2512.08987v1,arxiv,,"Inverse design aims to design the input variables of a physical system to optimize a specified objective function, typically formulated as a search or optimization problem. However, in 3D domains, the design space grows exponentially, rendering exhaustive grid-based searches infeasible. Recent advan"
266,,"Symmetry, Invariant Manifolds and Flow Reversals in Active Nematic Turbulence",Angel Naranjo; Rumayel Pallock; Caleb Wagner; Piyush Grover,2025,arXiv,,,,,0,0.000,0.182,,http://arxiv.org/abs/2512.07047v1,https://arxiv.org/pdf/2512.07047v1,arxiv,,"We investigate how symmetry, exact coherent structures (ECSs), and their invariant manifolds organize spontaneous flow reversals in a 2D active nematic confined to a periodic channel. In minimal flow units commensurate with the intrinsic active vortex scale, we use equivariant bifurcation theory to "
267,,Octahedral rotation instability in Ba$_2$IrO$_4$,Alaska Subedi,2025,arXiv,,,,,0,0.000,0.182,,http://arxiv.org/abs/2512.23690v1,https://arxiv.org/pdf/2512.23690v1,arxiv,,"Ba$_2$IrO$_4$ has been refined in the tetragonal $I4/mmm$ phase without octahedral rotations, and its physical properties have been interpreted in this high-symmetry structure. However, the dynamical stability of this undistorted phase has not previously been questioned. It is important to establish"
268,,SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection,Ido Nitzan HIdekel; Gal lifshitz; Khen Cohen; Dan Raviv,2025,arXiv,,,,,0,0.000,0.182,,http://arxiv.org/abs/2511.21325v1,https://arxiv.org/pdf/2511.21325v1,arxiv,,"Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs. A central reason is spectral bias, the tendency of neural networks to learn low-frequency structure before high-frequency (HF) details, which both causes DF generators to leave HF artifacts and leaves those sam"
269,,State-Based AI Backbone (Neural State Spaces),"Liu, Ran",2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.180,10.5281/zenodo.17885668,https://openalex.org/W7114769260,https://doi.org/10.5281/zenodo.17885668,openalex,,"This figure expands the state‑based view of the Foundational AI Backbone from the “Cognitive Hologram of AI Knowledge” (DOI: 10.5281/zenodo.17860776). While the process‑based backbone (DOI: 10.5281/zenodo.17874215) focuses on when and in what order things happen, the state‑based backbone focuses on "
270,,Enhancing Monkeypox Diagnosis with Transformers: Bridging Explainability and Performance with Quantitative Validation,Delal Şeker; Abdulnasır Yildiz,2025,Diagnostics,,,,,0,0.000,0.180,10.3390/diagnostics15182354,https://openalex.org/W4414239792,https://www.mdpi.com/2075-4418/15/18/2354/pdf?version=1758032312,openalex,,"Background/Objectives: Monkeypox is a zoonotic virus that presents with smallpox-like symptoms, making visual diagnosis challenging due to overlap with other dermatological conditions. Existing AI-based studies on monkeypox classification have largely relied on Convolutional Neural Networks (CNNs), "
271,,Rectifying Multi-Attack Adversarial Perturbations in Deep Neural Network based Image Classifier,Yulong Wang; Jiaxuan Song; Tianxiang Li; Xin Yuan; Hong Li,2025,ACM Transactions on Privacy and Security,,,,,0,0.000,0.180,10.1145/3765757,https://openalex.org/W4413941654,https://dl.acm.org/doi/pdf/10.1145/3765757,openalex,,"Deep neural networks (DNNs) for image classification remain vulnerable to adversarial perturbations–subtle input manipulations that induce catastrophic misclassifications. To address this issue, we propose the Adversarial Image Rectifier (AIR), a linguistically inspired detection and mitigation fram"
272,,Empirical Evidence for AI Consciousness and the Risks of its Current Socialization,Maggie Vale,2025,,,,,,0,0.000,0.180,10.36227/techrxiv.175203764.42125626/v2,https://openalex.org/W4413972633,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175203764.42125626/v2,openalex,,
273,,MATRIX-MFO Tandem Workshop: Nonlinear Geometric Diffusion Equations,Theodora Bourni; Mat Langford; Julian Scheuer; Miles Simon,2025,Oberwolfach Reports,,,,,0,0.000,0.180,10.4171/owr/2025/11,https://openalex.org/W4413812903,https://ems.press/content/serial-article-files/51357,openalex,,"This tandem workshop with MATRIX in Creswick, Australia, brought together leading experts from the fields of geometric partial differential equations and geometric analysis in general. The focus of the workshop was on recent developments and directions in non-linear geometric diffusion equations. Th"
274,,Investigating the Possibility of Integrating Quantum Mechanics with General Relativity Through a Novel Way of Treating Time,Georgios Alamanos,2025,Preprints.org,,,,,0,0.000,0.180,10.20944/preprints202501.2149.v4,https://openalex.org/W4413688587,https://www.preprints.org/frontend/manuscript/526dbcb60342ac7e929f3fc500ad27aa/download_pub,openalex,,"In physics, the two most successful theories, quantum mechanics and general relativity, appear to be incompatible with each other. Many theorists believe that the reason behind this, is that these theories treat space and time very differently, thus focus their attempts on finding a new way of model"
275,,Can a Novel Reinterpretation of Time Provide the Framework for Integrating Quantum Mechanics and General Relativity?,Georgios Alamanos,2025,Preprints.org,,,,,0,0.000,0.180,10.20944/preprints202508.0293.v1,https://openalex.org/W4413032139,https://www.preprints.org/frontend/manuscript/73698360458dc1365b2ba819a5b99cd9/download_pub,openalex,,"In physics, the two most successful theories, quantum mechanics and general relativity, appear to be incompatible with each other. Many theorists believe that the reason behind this, is that these theories treat space and time very differently, thus focus their attempts on finding a new way of model"
276,,Incremental Learning-enabled Fault Diagnosis of Dynamic Systems: A Comprehensive Review,Zeyi Liu; Xiao He; Biao Huang; Donghua Zhou,2025,,,,,,0,0.000,0.180,10.36227/techrxiv.175423977.79569757/v1,https://openalex.org/W4412870338,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175423977.79569757,openalex,,
277,,Surface-based Molecular Design with Multi-modal Flow Matching,Fang Wu; Zhengyuan Zhou; Shuting Jin; Xianfa Zeng; Jure Leskovec,2025,,,,,,0,0.000,0.180,10.1145/3711896.3737139,https://openalex.org/W4412876964,https://dl.acm.org/doi/pdf/10.1145/3711896.3737139,openalex,,
278,,WITHDRAWN,Jian‐Sheng Kang,2025,,,,,,0,0.000,0.180,10.31234/osf.io/jy3st_v7,https://openalex.org/W4412843216,https://osf.io/jy3st_v7/download,openalex,,
279,,"Fiber Angle Dynamics on S³: A Geometric Origin for Flavor Mixing, CP Violation, and Fermion Generations",Bin Li,2025,Preprints.org,,,,,0,0.000,0.180,10.20944/preprints202507.2441.v1,https://openalex.org/W4412813534,https://www.preprints.org/frontend/manuscript/76b2f9bdfcb0b7ae1a316ca630ac6174/download_pub,openalex,,"We propose a geometric-topological framework in which fermion flavor mixing, confinement, CP violation, and the existence of exactly three generations arise from the dynamics of an internal \( S^3 \) fiber space over Lorentzian spacetime. A unit-norm vector field—the \emph{chronon}—maps each spaceti"
280,,‘I Have Seen the Sea’: Caribbean Aquatic Poetics in Monique Roffey’s The Mermaid of Black Conch,Leighan Renaud,2025,Humanities,,,,,0,0.000,0.180,10.3390/h14070154,https://openalex.org/W4412512952,https://www.mdpi.com/2076-0787/14/7/154/pdf?version=1753065250,openalex,,"The polyvalent nature of water is one often explored in fiction by Caribbean writers, and this paper will consider the ways that the representations of mermaids act as an extension of this exploration. Mermaids are central to a number of folk traditions across the Caribbean region and its diaspora. "
281,,Application of Image Computing in Non-Destructive Detection of Chinese Cuisine,Xiaowei Huang; Zexiang Li; Zhihua Li; Jiyong Shi; Ning Zhang,2025,Foods,,,,,0,0.000,0.180,10.3390/foods14142488,https://openalex.org/W4412480812,https://www.mdpi.com/2304-8158/14/14/2488/pdf?version=1752671430,openalex,,"Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-de"
282,,"Optimization, Communication, and Personalization in Federated Learning for Massive Networks",Sameera Gallus; Aidan Mercer; Priya Singh; Daniel Cho,2025,Preprints.org,,,,,0,0.000,0.180,10.20944/preprints202507.1037.v1,https://openalex.org/W4412410315,https://www.preprints.org/frontend/manuscript/65c69c3a66ef754bf61740630160b483/download_pub,openalex,,"We consider the problem of collaborative model optimization over a distributed network of agents, each possessing locally held data drawn from potentially heterogeneous distributions. The system operates under constraints of limited communication, partial participation, and privacy preservation, the"
283,,A Cyber-Physical Model of the Buga Sphere: Unifying Anomalous Dynamics through a Topo-Temporal Photonic-Neural Architecture,Patrick Morcillo,2025,,,,,,0,0.000,0.180,10.31219/osf.io/eukjb_v1,https://openalex.org/W4412116456,https://osf.io/eukjb_v1/download,openalex,,"The Buga Sphere is a physical artifact whose observed properties—including non-ejective propulsion, a drastic $\approx$8.1\,kg apparent mass change, and a sustained 100\,W endothermic signature—are mutually contradictory within any known physical or engineering framework \cite{BugaSphereDataSource}."
284,,Coverage-Guided Testing for Deep Learning Models: A Comprehensive Survey,Zhiqiu Huang,2025,arXiv (Cornell University),,,,,0,0.000,0.180,10.48550/arxiv.2507.00496,https://openalex.org/W4416881150,https://arxiv.org/pdf/2507.00496,openalex,,"As Deep Learning (DL) models are increasingly applied in safety-critical domains, ensuring their quality has emerged as a pressing challenge in modern software engineering. Among emerging validation paradigms, coverage-guided testing (CGT) has gained prominence as a systematic framework for identify"
285,,GANs Secretly Perform Approximate Bayesian Model Selection,Marius P. Linhard,2025,arXiv (Cornell University),,,,,0,0.000,0.180,10.48550/arxiv.2507.00651,https://openalex.org/W4416887533,https://arxiv.org/pdf/2507.00651,openalex,,"Generative Adversarial Networks (GANs) are popular and successful generative models. Despite their success, optimization is notoriously challenging and they require regularization against overfitting. In this work, we explain the success and limitations of GANs by interpreting them as probabilistic "
286,,Celestial Chiral Algebras and Self-Dual Gravity,"Heuveline, Simon",2025,arXiv (Cornell University),,,,,0,0.000,0.180,10.48550/arxiv.2507.00772,https://openalex.org/W4416888435,https://arxiv.org/pdf/2507.00772,openalex,,"Celestial holography suggests, among other things, that collinear singularities of graviton scattering amplitudes are described by the OPEs of some putative dual CFT. One of the great successes has been the insight that this duality is true at tree-level which led to the discovery of new infinite di"
287,,Realizability in tropical geometry and unobstructedness of Lagrangian submanifolds,J. Hicks,2025,Geometry & Topology,,,,,0,0.000,0.180,10.2140/gt.2025.29.1909,https://openalex.org/W4411849742,https://msp.org/gt/2025/29-4/gt-v29-n4-p04-s.pdf,openalex,,
288,,Report on 2503.08657v1,,2025,,,,,,0,0.000,0.180,10.21468/scipost.report.11463,https://openalex.org/W4413943194,https://arxiv.org/pdf/2503.08657v1.pdf,openalex,,
289,,Semi-Blind Receivers for Uniform Rectangular Arrays - A Block-Term Decomposition-based Approach,Eleftherios Kofidis,2025,,,,,,0,0.000,0.180,10.36227/techrxiv.175037505.51768853/v1,https://openalex.org/W4411442779,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175037505.51768853/v1,openalex,,
290,,Geometry: The Interface of Consciousness and Reality in the Quantum-Conscious Nexus,David R. Mitchell,2025,,,,,,0,0.000,0.180,10.31219/osf.io/73jqz_v2,https://openalex.org/W4411274436,https://osf.io/73jqz_v2/download,openalex,,"The Quantum-Conscious Nexus (QCN) framework posits a primordial, pre-geometric topological substrate—the Nexus—from which spacetime and physical law emerge via Free Energy Principle (FEP)-driven mechanics involving predictive conscious systems. This paper explores the hypothesis that specific classe"
291,,Exploring Neural Mechanisms Underlying High-Dimensional Brain Activity,Arturo Tozzi,2025,Preprints.org,,,,,0,0.000,0.180,10.20944/preprints202506.0434.v1,https://openalex.org/W4411047609,https://www.preprints.org/frontend/manuscript/dcc173b69c200e594bb5b5681fdcc67c/download_pub,openalex,,"Understanding whether and how the central nervous system processes information beyond conventional 3D space could shed light on how the brain integrates complex information, supports flexible behaviour and enables abstract reasoning. Building on prior work with topological charge pumps, we explore 2"
292,,"The hyperplane string, RCFTs, and the swampland",Luca Novelli,2025,arXiv (Cornell University),,,,,0,0.000,0.180,10.48550/arxiv.2506.05173,https://openalex.org/W4416137828,https://arxiv.org/pdf/2506.05173,openalex,,"Six dimensional $\mathcal{N}=(1,0)$ supergravity features BPS strings whose properties encode highly nontrivial information about the parent 6d theory. We focus on a distinguished set of theories whose string charge lattice is one-dimensional. In geometric theories, the generator of the lattice aris"
293,,"Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence",John Violos; Konstantina-Christina Diamanti; Ioannis Kompatsiaris; Symeon Papadopoulos,2025,arXiv (Cornell University),,,,,0,0.000,0.180,10.48550/arxiv.2506.01869,https://openalex.org/W4414898245,https://arxiv.org/pdf/2506.01869,openalex,,"Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field aims to achieve acceptable performance while minimizing the use of computational resources, time, energy, and data for bo"
294,,Enhancing Visual Re-Ranking Through Denoising Nearest Neighbor Graph via Continuous CRF,Jaeyoon Kim; Yoonki Cho; Taeyong Kim; Sung-eui Yoon,2024,International Conference on Information Photonics,,,,,0,0.000,0.171,10.1109/icip55913.2025.11084658,https://www.semanticscholar.org/paper/851a21693baa922bfc0224c8d35219c5f9c6db48,,semantic_scholar,,"Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy, offering the advantages of effectively exploring high-dimensional manifolds without requiring additional fine-tuning. However, the effectiveness of NN graph-based re-ranking is fu"
295,,Imagining Indian Nation-State: Rereading Qurratulain Hyder’s Select Novels in Contemporary Scenario,SK Sagir Ali,2023,Southeast Asian Review of English,,,,,0,0.000,0.150,10.22452/sare.vol60no2.6,https://www.semanticscholar.org/paper/81ec190912c91d35ad9f8da67515593bbb28d1fd,https://sare.um.edu.my/index.php/SARE/article/download/46065/16548,semantic_scholar,,"Given the contemporary hyper-nationalist ambiance in the Indian subcontinent, the reading of Qurratulain Hyder is significant, especially from the decolonial nationalist perspective of her selected translated Urdu novels. The paper examines the events and metaphors in both novels through a decolonia"
296,,Classifying bi-invariant 2-forms on infinite-dimensional Lie groups,David Michael Roberts,2023,,,,,,0,0.000,0.145,,https://www.semanticscholar.org/paper/e2e08eb06291f1b7dd89f9592079745ca0170bda,,semantic_scholar,,"A bi-invariant differential 2-form on a Lie group G is a highly constrained object, being determined by purely linear data: an Ad-invariant alternating bilinear form on the Lie algebra of G. On a compact connected Lie group these have an known classification, in terms of de Rham cohomology, which is"
297,,Higher category theory and n-groups as gauge symmetries for quantum gravity,B. Nikolić; D. Obrić; T. Radenković; Igor Salom; M. Vojinović,2023,Journal of Physics: Conference Series,,,,,0,0.000,0.144,10.1088/1742-6596/2667/1/012019,https://www.semanticscholar.org/paper/43d5b6da123511d92659e4c0521b73593c8edbc2,https://iopscience.iop.org/article/10.1088/1742-6596/2667/1/012019/pdf,semantic_scholar,,"Higher category theory can be employed to generalize the notion of a gauge group to the notion of a gauge n-group. This novel algebraic structure is designed to generalize notions of connection, parallel transport and holonomy from curves to manifolds of dimension higher than one. Thus it generalize"
298,,Data Decomposition for Constrained Visual Learning,Calvin Murdock,2021,,,,,,0,0.000,0.123,10.1184/r1/13557188.v1,https://www.semanticscholar.org/paper/dacdd39afbc3c27ca122634b19cacae1f72e7128,,semantic_scholar,,
299,,Study of Hypersurface of semi-almost Hermitian manifold equipped with quarter-symmetric non-metric connection,Pankaj Pandey; B. B. Chaturvedi; Ejaz Sabir Lone,2020,Journal of Physics: Conference Series,,,,,0,0.000,0.108,10.1088/1742-6596/1531/1/012051,https://www.semanticscholar.org/paper/8e1abe414f9ecffe4077b17ad44f01f5eb62257a,https://doi.org/10.1088/1742-6596/1531/1/012051,semantic_scholar,,"In this paper, an induced connection on a Hyper surface ofa semi-almost Hermitian manifold equipped with a quarter-symmetric non-metric connection is studied and proved that induced connection is also a quarter-symmetricnon-metric connection.Further, we have obtained We ingarten equation, equation o"
300,,Plato Under Review: What Is Going Wrong in Academic Philosophical Writing,Giacomo Pezzano,2025,Humanities,,,,,2,0.000,0.000,10.3390/h14060116,https://openalex.org/W4410860004,https://www.mdpi.com/2076-0787/14/6/116/pdf?version=1748521845,openalex,,"This paper addresses the problem of stylistic pluralism in philosophical writing, arguing that its progressive narrowing to the form of the paper is not just an esthetic issue but can also have negative effects on the development of academic research itself. The contribution is divided into two part"
301,,"Noncoherent MIMO Communications: Theoretical Foundation, Design Approaches, and Future Challenges",Khac–Hoang Ngo; Diego Cuevas; Ruben de Miguel Gil; Víctor Monzón Baeza; Ignacio Santamarı́a,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2505.23172,https://openalex.org/W4416610237,https://arxiv.org/pdf/2505.23172,openalex,,"Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive d"
302,,"‘Integration-through-Law’: grand theory, revisionist history",Robert Schütze,2025,European Law Open,,,,,0,0.000,0.000,10.1017/elo.2025.15,https://openalex.org/W4410790534,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F750BAFE5ECAC17C67D6667B85DCDFA1/S2752613525000153a.pdf/div-class-title-integration-through-law-grand-theory-revisionist-history-div.pdf,openalex,,"Abstract How has the European Union been integrated in the past? Legal academics have traditionally pointed to the Court of Justice and to the broader idea of an ‘integration-through-law’. Through its supranational jurisprudence, the Court – not the EU legislature – was thus placed at the centre of "
303,,Finding the right path: statistical mechanics of connected solutions in constraint satisfaction problems,Damien Barbier,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2505.20954,https://openalex.org/W4415036511,https://arxiv.org/pdf/2505.20954,openalex,,"We define and study a statistical mechanics ensemble that characterizes connected solutions in constraint satisfaction problems (CSPs). Built around a well-known local entropy bias, it allows us to better identify hardness transitions in problems where the energy landscape is dominated by isolated s"
304,,Learning Approaches to Dynamic Workflow Scheduling based on Genetic Programming and Deep Reinforcement Learning,Yifan Yang,2025,,,,,,0,0.000,0.000,10.26686/wgtn.29134007,https://openalex.org/W4410700106,https://openaccess.wgtn.ac.nz/articles/thesis/Learning_Approaches_to_Dynamic_Workflow_Scheduling_based_on_Genetic_Programming_and_Deep_Reinforcement_Learning/29134007/1/files/54766925.pdf,openalex,,"&lt;p&gt;&lt;strong&gt;Dynamic workflow scheduling (DWS) in cloud computing is a critical yet challenging problem, involving assigning numerous workflow tasks to heterogeneous virtual machines under dynamic conditions to optimize cost or makespan. The complexity arises from unpredictable workflow ar"
305,,Randomization Times under Quantum Chaotic Hamiltonian Evolution,Souradeep Ghosh; Nicholas Hunter-Jones; Joaquin F. Rodriguez-Nieva,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25074v1,https://arxiv.org/pdf/2512.25074v1,arxiv,,"Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics suc"
306,,GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction,Yi-Chuan Huang; Hao-Jen Chien; Chin-Yang Lin; Ying-Huan Chen; Yu-Lun Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25073v1,https://arxiv.org/pdf/2512.25073v1,arxiv,,"Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to"
307,,Edit3r: Instant 3D Scene Editing from Sparse Unposed Images,Jiageng Liu; Weijie Lyu; Xueting Li; Yejie Guo; Ming-Hsuan Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25071v1,https://arxiv.org/pdf/2512.25071v1,arxiv,,"We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealisti"
308,,Coordinated Humanoid Manipulation with Choice Policies,Haozhi Qi; Yen-Jen Wang; Toru Lin; Brent Yi; Yi Ma,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25072v1,https://arxiv.org/pdf/2512.25072v1,arxiv,,"Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address t"
309,,Classification of Interacting Topological Crystalline Superconductors in Three Dimensions and Beyond,Shang-Qiang Ning; Xing-Yu Ren; Qing-Rui Wang; Yang Qi; Zheng-Cheng Gu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25069v1,https://arxiv.org/pdf/2512.25069v1,arxiv,,"Although classification for free-fermion topological superconductors (TSC) is established, systematically understanding the classification of 3D interacting TSCs remains difficult, especially those protected by crystalline symmetries like the 230 space groups. We build up a general framework for sys"
310,,No-cost Bell Nonlocality Certification from Quantum Tomography and Its Applications in Quantum Magic Witnessing,Pawel Cieslinski; Lukas Knips; Harald Weinfurter; Wieslaw Laskowski,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25068v1,https://arxiv.org/pdf/2512.25068v1,arxiv,,"Tomographic measurements are the standard tool for characterizing quantum states, yet they are usually regarded only as means for state reconstruction or fidelity measurement. Here, we show that the same Pauli-basis measurements (X, Y, Z) can be directly employed for the certification of nonlocality"
311,,FineTec: Fine-Grained Action Recognition Under Temporal Corruption via Skeleton Decomposition and Sequence Completion,Dian Shao; Mingfei Shi; Like Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25067v1,https://arxiv.org/pdf/2512.25067v1,arxiv,,"Recognizing fine-grained actions from temporally corrupted skeleton sequences remains a significant challenge, particularly in real-world scenarios where online pose estimation often yields substantial missing data. Existing methods often struggle to accurately recover temporal dynamics and fine-gra"
312,,From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing,Xu He; Haoxian Zhang; Hejia Chen; Changyuan Zheng; Liyang Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25066v1,https://arxiv.org/pdf/2512.25066v1,arxiv,,"Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this wit"
313,,The Logical Structure of Physical Laws: A Fixed Point Reconstruction,Eren Volkan Küçük,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25057v1,https://arxiv.org/pdf/2512.25057v1,arxiv,,"We formalise the self referential definition of physical laws using monotone operators on a lattice of theories, resolving the pathologies of naive set theoretic formulations. By invoking Tarski fixed point theorem, we identify physical theories as least fixed points of admissibility constraints der"
314,,Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework,Liliang Wang; Alex Gorodetsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25056v1,https://arxiv.org/pdf/2512.25056v1,arxiv,,"Online joint estimation of unknown parameters and states in a dynamical system with uncertainty quantification is crucial in many applications. For example, digital twins dynamically update their knowledge of model parameters and states to support prediction and decision-making. Reliability and comp"
315,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25055v1,https://arxiv.org/pdf/2512.25055v1,arxiv,,This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three 
316,,AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG,Chao Peng; Bin Wang; Zhilei Long; Jinfang Sheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25052v1,https://arxiv.org/pdf/2512.25052v1,arxiv,,"Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for"
317,,The PDE-ODI principle and cylindrical mean curvature flows,Richard H. Bamler; Yi Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25050v1,https://arxiv.org/pdf/2512.25050v1,arxiv,,"We introduce a new approach for analyzing ancient solutions and singularities of mean curvature flow that are locally modeled on a cylinder. Its key ingredient is a general mechanism, called the \emph{PDE--ODI principle}, which converts a broad class of parabolic differential equations into systems "
318,,Compound Estimation for Binomials,Yan Chen; Lihua Lei,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25042v1,https://arxiv.org/pdf/2512.25042v1,arxiv,,"Many applications involve estimating the mean of multiple binomial outcomes as a common problem -- assessing intergenerational mobility of census tracts, estimating prevalence of infectious diseases across countries, and measuring click-through rates for different demographic groups. The most standa"
319,,Perturbative Kondo destruction and global phase diagram of heavy fermion metals,Yiming Wang; Shouvik Sur; Chia-Chuan Liu; Qimiao Si,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25036v1,https://arxiv.org/pdf/2512.25036v1,arxiv,,"Strange metals represent a foundational problem in quantum condensed matter physics, and heavy fermion systems provide a canonical setting to advance a general understanding. The concept of a Kondo destruction quantum critical point is widely invoked to describe the competition of the Kondo effect a"
320,,Large Neutrino-Dark Matter Interactions: From Effective Field Theory to Ultraviolet Completions,K. S. Babu; P. S. Bhupal Dev; Anil Thapa,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25035v1,https://arxiv.org/pdf/2512.25035v1,arxiv,,"We develop a general effective field theory (EFT) framework for neutrino-dark matter (DM) interactions, and apply it to systematically find all possible gauge-invariant ultraviolet (UV) completions at a given EFT operator dimension. Our goal here is to find simple UV-complete models that can realize"
321,,Universal Seesaw Pati-Salam Model with P for Strong CP,K. S. Babu; Sumit Biswas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25028v1,https://arxiv.org/pdf/2512.25028v1,arxiv,,"We develop a universal seesaw version of the Pati-Salam model wherein quarks and leptons of each family are unified into common multiplets transforming as $ψ_L(2,1,4))+ ψ_R((1,2,4)$ under the $SU(2)_L \times SU(2)_R \times SU(4)_c$ gauge symmetry. Parity symmetry is spontaneously broken in the model"
322,,Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension,Omar Said; Christopher Tossas-Betancourt; Mary K. Olive; Jimmy C. Lu; Adam Dorfman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25027v1,https://arxiv.org/pdf/2512.25027v1,arxiv,,"Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease that leads to increased pulmonary pressures, vascular remodeling, and eventual right ventricular (RV) failure. Pediatric PAH remains understudied due to limited data and the lack of targeted diagnostic and therapeutic str"
323,,Modewise Additive Factor Model for Matrix Time Series,Elynn Chen; Yuefeng Han; Jiayu Li; Ke Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25025v1,https://arxiv.org/pdf/2512.25025v1,arxiv,,"We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offering greater flexibility than multiplicative frameworks such as Tucker and CP factor models. In MAFM, each observation d"
324,,MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes,Siddhant Agarwal; Adya Dhuler; Polly Ruhnke; Melvin Speisman; Md Shad Akhtar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25015v1,https://arxiv.org/pdf/2512.25015v1,arxiv,,"Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive sympto"
325,,Diffusion Language Models are Provably Optimal Parallel Samplers,Haozhe Jiang; Nika Haghtalab; Lijie Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25014v1,https://arxiv.org/pdf/2512.25014v1,arxiv,,Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundation for this advantage by formalizing a model of parallel sampling and showing that DLMs augmented with polynomial-length
326,,At the intersection of Numerical Analysis and Spectral Geometry,Nilima Nigam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25012v1,https://arxiv.org/pdf/2512.25012v1,arxiv,,"How do the geometric properties of a domain impact the spectrum of an operator defined on it? How do we compute accurate and reliable approximations of these spectra? The former question is studied in spectral geometry, and the latter is a central concern in numerical analysis. In this short exposit"
327,,Uniqueness for stochastic differential equations in Hilbert spaces with irregular drift,Lukas Anzeletti; Oleg Butkovsky; Máté Gerencsér; Alexander Shaposhnikov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25003v1,https://arxiv.org/pdf/2512.25003v1,arxiv,,"We present a versatile framework to study strong existence and uniqueness for stochastic differential equations (SDEs) in Hilbert spaces with irregular drift. We consider an SDE in a separable Hilbert space $H$ \begin{equation*} dX_t= (A X_t + b(X_t))dt +(-A)^{-γ/2}dW_t,\quad X_0=x_0 \in H, \end{equ"
328,,The local limit of weighted spanning trees on balanced networks,Ágnes Kúsz,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25001v1,https://arxiv.org/pdf/2512.25001v1,arxiv,,"We prove that the local limit of the weighted spanning trees on any simple connected high degree almost regular sequence of electric networks is the Poisson(1) branching process conditioned to survive forever, by generalizing [NP22] and closing a gap in their proof. We also study the local statistic"
329,,Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification,Zhenyu Cui; Jiahuan Zhou; Yuxin Peng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25000v1,https://arxiv.org/pdf/2512.25000v1,arxiv,,"Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-R"
330,,Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis,Seunghoon Paik; Kangjie Zhou; Matus Telgarsky; Ryan J. Tibshirani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24999v1,https://arxiv.org/pdf/2512.24999v1,arxiv,,"We introduce \textit{basic inequalities} for first-order iterative optimization algorithms, forming a simple and versatile framework that connects implicit and explicit regularization. While related inequalities appear in the literature, we isolate and highlight a specific form and develop it as a w"
331,,Numerical study of boson mixtures with multi-component continuous matrix product states,Wei Tang; Benoît Tuybens; Jutho Haegeman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24998v1,https://arxiv.org/pdf/2512.24998v1,arxiv,,"The continuous matrix product state (cMPS) ansatz is a promising numerical tool for studying quantum many-body systems in continuous space. Although it provides a clean framework that allows one to directly simulate continuous systems, the optimization of cMPS is known to be a very challenging task,"
332,,Manifold classification from the descriptive viewpoint,Jeffrey Bergfalk; Iian B. Smythe,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24996v1,https://arxiv.org/pdf/2512.24996v1,arxiv,,"We consider classification problems for manifolds and discrete subgroups of Lie groups from a descriptive set-theoretic point of view. This work is largely foundational in conception and character, recording both a framework for general study and Borel complexity computations for some of the most fu"
333,,Strong Gravitational Lensing by a Black Hole with a Global Monopole in Kalb-Ramond Bumblebee Gravity,Bijendra Kumar Vishvakarma; Shubham Kala,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24995v1,https://arxiv.org/pdf/2512.24995v1,arxiv,,"We investigate the strong gravitational lensing and shadow properties of the black hole in the context of bumblebee gravity, characterized by a global monopole charge $κη^2$ and a Lorentz symmetry breaking parameter $γ$. We compute the deflection angles of light passing near the black hole in strong"
334,,Mathieu Control of the Effective Coupling in Superconducting Qubits,Yi-Han Yu; Xin-Yi Li; Kai Xu; Heng Fan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24992v1,https://arxiv.org/pdf/2512.24992v1,arxiv,,"A common challenge in superconducting quantum circuits is the trade-off between strong coupling and computational subspace integrity. We present Mathieu control, which uses a non-resonant two-photon drive to create a selective nonlinear frequency shift. This shift modifies interactions while preserv"
335,,Best Practices for Modelling Electrides,Lee A. Burton,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24989v1,https://arxiv.org/pdf/2512.24989v1,arxiv,,"Materials in which electrons occupy interstitial sites as anions are called electrides and exhibit unusual dimensionality-dependent electronic behavior. These properties make electrides attractive for catalysis, transparent conductors, and emergent quantum phenomena, yet their theoretical treatment "
336,,PhysTalk: Language-driven Real-time Physics in 3D Gaussian Scenes,Luca Collorone; Mert Kiray; Indro Spinelli; Fabio Galasso; Benjamin Busam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24986v1,https://arxiv.org/pdf/2512.24986v1,arxiv,,"Realistic visual simulations are omnipresent, yet their creation requires computing time, rendering, and expert animation knowledge. Open-vocabulary visual effects generation from text inputs emerges as a promising solution that can unlock immense creative potential. However, current pipelines lack "
337,,Lindbladian PT phase transitions,Yuma Nakanishi; Tomohiro Sasamoto,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24981v1,https://arxiv.org/pdf/2512.24981v1,arxiv,,"A parity-time (PT) transition is a spectral transition characteristic of non-Hermitian generators; it typically occurs at an exceptional point, where multiple eigenvectors coalesce. The concept of a PT transition has been extended to Markovian open quantum systems, which are described by the GKSL eq"
338,,SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets,Barna Zajzon; Younes Bouhadjar; Maxime Fabre; Felix Schmidt; Noah Ostendorf,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24977v1,https://arxiv.org/pdf/2512.24977v1,arxiv,,"Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of great importance to develop framew"
339,,Graphicality of power-law and double power-law degree sequences,Pietro Valigi; M. Ángeles Serrano; Claudio Castellano; Lorenzo Cirigliano,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24976v1,https://arxiv.org/pdf/2512.24976v1,arxiv,,"The graphicality problem -- whether or not a sequence of integers can be used to create a simple graph -- is a key question in network theory and combinatorics, with many important practical applications. In this work, we study the graphicality of degree sequences distributed as a power-law with a s"
340,,Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments,Yunxi Tang; Tianqi Yang; Jing Huang; Xiangyu Chu; Kwok Wai Samuel Au,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24974v1,https://arxiv.org/pdf/2512.24974v1,arxiv,,"Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning an"
341,,GEQIE Framework for Rapid Quantum Image Encoding,Rafał Potempa; Michał Kordasz; Józef P. Cyran; Kamil Wereszczyński; Krzysztof Simiński,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24973v1,https://arxiv.org/pdf/2512.24973v1,arxiv,,"This work presents a Python framework named after the General Equation of Quantum Image Encoding (GEQIE). The framework creates the image-encoding state using a unitary gate, which can later be transpiled to target quantum backends. The benchmarking results, simulated with different noise levels, de"
342,,From Complex-Analytic Models to Sparse Domination: A Dyadic Approach of Hypersingular Operators via Bourgain's Interpolation Method,Bingyang Hu; Xiaojing Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24972v1,https://arxiv.org/pdf/2512.24972v1,arxiv,,"Motivated by the work of Cheng--Fang--Wang--Yu on the hypersingular Bergman projection, we develop a real-variable and dyadic framework for hypersingular operators in regimes where strong-type estimates fail at the critical line. The main new input is a hypersingular sparse domination principle comb"
343,,Random Batch Sum-of-Gaussians Method for Molecular Dynamics of Born-Mayer-Huggins Systems,Chen Chen; Jiuyang Liang; Zhenli Xu; Qianru Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24970v1,https://arxiv.org/pdf/2512.24970v1,arxiv,,"The Born-Mayer-Huggins (BMH) potential, which combines Coulomb interactions with dispersion and short-range exponential repulsion, is widely used for ionic materials such as molten salts. However, large-scale molecular dynamics simulations of BMH systems are often limited by computation, communicati"
344,,Cosmic Himalayas in CROCODILE : Probing the Extreme Quasar Overdensities by Count-in-Cells analysis and Nearest Neighbor Distribution,Yuto Kuwayama; Yongming Liang; Kentaro Nagamine; Yuri Oku; Daisuke Nishihama,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24966v1,https://arxiv.org/pdf/2512.24966v1,arxiv,,"The recently reported Cosmic Himalayas (CH) -- an extreme quasar overdensity at z~2 -- poses an apparent challenge to the Lambda CDM framework, with a reported significance of 16.9-sigma under Gaussian assumptions. Such an event appears improbably rare, with a formal probability of P ~ 10^-68. In th"
345,,Approximating evolution operators of linear delay equations: a general framework for the convergence analysis,Alessia andò; Giusy Bosco; Dimitri Breda; Davide Liessi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24964v1,https://arxiv.org/pdf/2512.24964v1,arxiv,,"We consider the problem of discretizing evolution operators of linear delay equations with the aim of approximating their spectra, which is useful in investigating the stability properties of (nonlinear) equations via the principle of linearized stability. We develop a general convergence analysis b"
346,,From Principles to Effective Models: A Constructive Framework for Effective Covariant Actions with a Unique Vacuum Solution,Kristina Giesel; Hongguang Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24960v1,https://arxiv.org/pdf/2512.24960v1,arxiv,,"The absence of Birkhoff's theorem in effective quantum gravity models leads to a fundamental ambiguity in the vacuum sector, where a priori no unique vacuum solution exists. As a result, phenomenological investigations of the physical implications of these models have been made more difficult. We ad"
347,,Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning,András Antos; András Millinghoffer; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24959v1,https://arxiv.org/pdf/2512.24959v1,arxiv,,"Many modern AI and ML problems require evaluating partners' contributions through shared yet asymmetric, computationally intensive processes and the simultaneous selection of the most beneficial candidates. Sequential approaches to these problems can be unified under a new framework, Sequential Supp"
348,,AMAP Agentic Planning Technical Report,Yulan Hu; Xiangwen Zhang; Sheng Ouyang; Hao Yi; Lu Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24957v1,https://arxiv.org/pdf/2512.24957v1,arxiv,,"We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and itinerary planning. STAgent is a specialized model capable of interacting with ten distinct tools within spatio-temporal"
349,,MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control,Yongwei Zhang; Yuanzhe Xing; Quan Quan; Zhikun She,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24955v1,https://arxiv.org/pdf/2512.24955v1,arxiv,,"Achieving provable stability in model-free reinforcement learning (RL) remains a challenge, particularly in balancing exploration with rigorous safety. This article introduces MSACL, a framework that integrates exponential stability theory with maximum entropy RL through multi-step Lyapunov certific"
350,,VIPER: Process-aware Evaluation for Generative Video Reasoning,Yifan Li; Yukai Gu; Yingqian Min; Zikang Liu; Yifan Du,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24952v1,https://arxiv.org/pdf/2512.24952v1,arxiv,,"Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation of continuous frames. While these models show promise for Generative Video Reasoning (GVR), existing evaluation framewor"
351,,"The uncertainty constants: A unified framework of two, three and four observables",Minyi Huang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24950v1,https://arxiv.org/pdf/2512.24950v1,arxiv,,"Uncertainty is a fundamental and important concept in quantum mechanics. Recent works have revealed both the product and sum forms of uncertainty constants for three observables. Such a result is intimately to the properties of Pauli operators. In this work, using the technique in matrix theory, we "
352,,ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT,Xinran Gong; Gorkem Durak; Halil Ertugrul Aktas; Vedat Cicek; Jinkui Hao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24948v1,https://arxiv.org/pdf/2512.24948v1,arxiv,,"Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respira"
353,,CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinement,Wentao Zhang; Tao Fang; Lina Lu; Lifei Wang; Weihe Zhong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24947v1,https://arxiv.org/pdf/2512.24947v1,arxiv,,"Accurate and interpretable crop disease diagnosis is essential for agricultural decision-making, yet existing methods often rely on costly supervised fine-tuning and perform poorly under domain shifts. We propose Caption--Prompt--Judge (CPJ), a training-free few-shot framework that enhances Agri-Pes"
354,,HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films,Rongji Xun; Junjie Yuan; Zhongjie Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24946v1,https://arxiv.org/pdf/2512.24946v1,arxiv,,"Existing open-source film restoration methods show limited performance compared to commercial methods due to training with low-quality synthetic data and employing noisy optical flows. In addition, high-resolution films have not been explored by the open-source methods.We propose HaineiFRDM(Film Res"
355,,Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes,Yuxuan Zhao; S. W. Wong,2024,Statistica sinica,,,,,2,0.000,0.000,10.5705/ss.202024.0213,https://www.semanticscholar.org/paper/59505531dc6e56a864c5a883b0860d09309620d3,,semantic_scholar,,"Dynamic systems described by differential equations often involve feedback among system components. When there are time delays for components to sense and respond to feedback, delay differential equation (DDE) models are commonly used. This paper considers the problem of inferring unknown system par"
356,,Absolute stability of program manifold of control systems with local connections,A. Tleulessova; S. Zhumatov; L. Zhapsarbayeva,2024,Bulletin of the National Engineering Academy of the Republic of Kazakhstan,,,,,1,0.000,0.000,10.47533/2024.1606-146x.025,https://www.semanticscholar.org/paper/87ec5d9a59f19e26d8d9e2290682ffd040cc33bd,,semantic_scholar,,The article presents the results carried out within the framework of the grant project of the Ministry of Science and Higher Education of the Republic of Kazakhstan AP19675193. In this paper we study the nonlinear problem of constructing material systems whose motions are described by ordinary diffe
357,,Recurrent connections enable point attractor dynamics and dimensionality reduction in a connectome-constrained model of the insect learning center,Justin Joyce; Raphael Norman-Tenazas; Patricia K. Rivlin; Grace M. Hwang; Isaac Western,2024,bioRxiv,,,,,0,0.000,0.000,10.1101/2024.01.10.574960,https://www.semanticscholar.org/paper/4877b7160bcde237a28d4448d0f7ad932ea919f0,https://www.biorxiv.org/content/biorxiv/early/2024/01/11/2024.01.10.574960.full.pdf,semantic_scholar,,"The learning center in the insect, the mushroom body (MB) with its predominant population of Kenyon Cells (KCs), is a widely studied model system to investigate neural processing principles, both experimentally and theoretically. While many computational models of the MB have been studied, the compu"
358,,Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF,Wei Xiong; Hanze Dong; Chen Ye; Han Zhong; Nan Jiang,2023,arXiv.org,,,,,65,0.000,0.000,10.48550/arXiv.2312.11456,https://www.semanticscholar.org/paper/e4435f282266da92d37066064c5239c6f96f0d64,,semantic_scholar,,
359,,Hyper-Raman Spectra in Solution Based on the Reference Interaction Site Model Self-Consistent Field Method Coupled with the Constrained Spatial Electron Density Distribution and Vibrational Quasi-Degenerate Perturbation Theory.,K. Suda; Kiyoshi Yagi; Daisuke Yokogawa,2025,Journal of Physical Chemistry Letters,,,,,0,0.000,0.000,10.1021/acs.jpclett.5c01961,https://www.semanticscholar.org/paper/2138811261773c1a6ce9c0c8f55d4178c18ed2ca,,semantic_scholar,,
360,,Quantum Manifold Optimization: A Design Framework for Future Communications Systems,Getuar Rexhepi; Hyeon Seok Rou; G. Abreu,2025,International Workshop on Signal Processing Advances in Wireless Communications,,,,,2,0.000,0.000,10.1109/SPAWC66079.2025.11143361,https://www.semanticscholar.org/paper/aac0611bf6e1ae1009e228ec918070fc36afcec4,,semantic_scholar,,"Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wire"
361,,Reconfiguring international manufacturing networks in times of uncertainty: towards a new theoretical framework,Eloi Letzelter; Zheng Liu; Yongjiang Shi; Bo Yang,2025,Business Process Management Journal,,,,,0,0.000,0.000,10.1108/bpmj-07-2025-1100,https://www.semanticscholar.org/paper/0e44eb72360f4ab0062c05ff5a93e01e3a7afb38,,semantic_scholar,,"
 
 Research on multinational enterprises (MNEs) is now more relevant than ever, as the world economy experiences financial stress spilling over into the real sector from the credit-constrained private sector, large trade imbalances from low public saving and high volatility in energy prices. Concer"
362,,Affordances for throwing: An uncontrolled manifold analysis,Timothy Bennett; Liam Thomas; Andrew D Wilson,2024,PLoS ONE,,,,,4,0.000,0.000,10.1371/journal.pone.0301320,https://www.semanticscholar.org/paper/2b97cf191adab80d66e22bce114728b40e61a704,https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0301320&type=printable,semantic_scholar,,"Movement systems are massively redundant, and there are always multiple movement solutions to any task demand; motor abundance. Movement consequently exhibits ‘repetition without repetition’, where movement outcomes are preserved but the kinematic details of the movement vary across repetitions. The"
363,,Subspace-Constrained Quadratic Matrix Factorization: Algorithm and Applications,Zheng Zhai; Xiaohui Li,2024,Pattern Recognition,,,,,0,0.000,0.000,10.48550/arXiv.2411.04717,https://www.semanticscholar.org/paper/6453ecb7dc383d3537438c939c70d95807e3cf63,,semantic_scholar,,"Matrix Factorization has emerged as a widely adopted framework for modeling data exhibiting low-rank structures. To address challenges in manifold learning, this paper presents a subspace-constrained quadratic matrix factorization model. The model is designed to jointly learn key low-dimensional str"
364,,HySim-LLM: Embedding-Weighted Fine-Tuning Bounds and Manifold Denoising for Domain-Adapted LLMs,Majid Jaberi Douraki; Hossein Sholehrasa; Xuan Xu; Remya Ampadi Ramachandran,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2510.07796,https://www.semanticscholar.org/paper/3a54754f255ab932783e1f1b3508cd038c0cfa2d,,semantic_scholar,,"The extraction and standardization of pharmacokinetic (PK) information from scientific literature remain significant challenges in computational pharmacology, which limits the reliability of data-driven models in drug development. Large language models (LLMs) have achieved remarkable progress in tex"
365,,GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation,Hana Satou; F. Monkey,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2505.15194,https://www.semanticscholar.org/paper/8f46ad9f995c5bec61085b5c36bf2d1b128be78d,,semantic_scholar,,"Domain adaptation remains a challenge when there is significant manifold discrepancy between source and target domains. Although recent methods leverage manifold-aware adversarial perturbations to perform data augmentation, they often neglect precise manifold alignment and systematic exploration of "
366,,Inequality Constraints on Statistical Submanifolds of Norden-Golden-like Statistical Manifold,Amit Kumar Rai; Majid Ali Choudhary; Mohammed Nisar; F. Aloui,2025,Symmetry,,,,,0,0.000,0.000,10.3390/sym17081206,https://www.semanticscholar.org/paper/b3afb6b2a6315b04ffb2d764f653dbaaa327bb9a,,semantic_scholar,,"This paper explores novel inequalities for statistical submanifolds within the framework of the Norden golden-like statistical manifold. By leveraging the intrinsic properties of statistical manifolds and the structural richness of Norden golden geometry, we establish fundamental relationships betwe"
367,,On the chemo-thermo-mechanics of constrained reactive mixtures of solids,Alberto Salvadori; M. Serpelloni; R. Mcmeeking,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/f719682c9b142da723f758856ffe0f47e9cb66db,,semantic_scholar,,"Building upon the classical chemo-mechanical theory of Larch{\'e} and Cahn for equilibrium, numerous studies have investigated the transport of species in solids, with or without trapping phenomena. In most applications -- such as the swelling of hydrogels, hydrogen embrittlement in metals, and the "
368,,Information Geometry of the Retinal Representation Manifold,Xuehao Ding; Dongsoo Lee; Joshua B. Melander; George Sivulka; S. Ganguli,2023,bioRxiv,,,,,13,0.000,0.000,10.1101/2023.05.17.541206,https://www.semanticscholar.org/paper/eeca6eb9453435f61e83744b1b135f2de9430ff7,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245665,semantic_scholar,,The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we pr
369,,Isometric Manifold Learning Using Hierarchical Flow,Ziqi Pan; Jianfu Zhang; Li Niu; Liqing Zhang,2023,AAAI Conference on Artificial Intelligence,,,,,1,0.000,0.000,10.1609/aaai.v37i8.26124,https://www.semanticscholar.org/paper/558e966b86efc2ae667115c1f769bab5955c1a1a,https://ojs.aaai.org/index.php/AAAI/article/download/26124/25896,semantic_scholar,,"We propose the Hierarchical Flow (HF) model constrained by isometric regularizations for manifold learning that combines manifold learning goals such as dimensionality reduction, inference, sampling, projection and density estimation into one unified framework. Our proposed HF model is regularized t"
370,,Federated PCA on Grassmann Manifold for IoT Anomaly Detection,Tung-Anh Nguyen; Long Tan Le; Tuan Dung Nguyen; Wei Bao; Suranga Seneviratne,2024,IEEE/ACM Transactions on Networking,,,,,16,0.000,0.000,10.1109/TNET.2024.3423780,https://www.semanticscholar.org/paper/2008b835a5ad1beeb54e570306ce7946ff47f28d,https://arxiv.org/pdf/2407.07421,semantic_scholar,,"With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection systems (ML-IDS) effectively employ supervised learni"
371,,Complex Neutrosophic Soft Topology with Multivariate Analysis: A Unified Framework for Signal-Template Relationships,Maha Mohammed Saeed; Raed Hatamleh; Hamza Ali Abujabal; Aqeedat Hussain; Arif Mehmood Khattak,2025,European Journal of Pure and Applied Mathematics,,,,,0,0.000,0.000,10.29020/nybg.ejpam.v18i4.7147,https://www.semanticscholar.org/paper/4e0f0c77e0055699928d5a30d4bf76fc53f639d1,,semantic_scholar,,"A novel theoretical method based on the topological formulation of complex neutrosophic soft sets (CNSS) is presented in this work. We go on to provide a thorough one-value complex neutrosophic soft topology, defining the most important topological properties such as interior, closure, and other rel"
372,,"Critical point search and linear response theory for computing electronic excitation energies of molecular systems. Part I: General framework, application to Hartree-Fock and DFT",L. Grazioli; Yukuan Hu; E. Cancès,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/654be9b7f8e18c772e59ae429f472aa83660ef81,,semantic_scholar,,"Computing excited states of many-body quantum Hamiltonians is a fundamental challenge in computational physics and chemistry, with state-of-the-art methods broadly classified into variational (critical point search) and linear response approaches. The K\""ahler manifold formalism provides a uniform f"
373,,RMVC: A Validated Algorithmic Framework for Decision-Making Under Uncertainty,Abdurrahman Dayıoğlu; Fatma Ozen Erdogan; B. Celik,2025,Mathematics,,,,,0,0.000,0.000,10.3390/math13162693,https://www.semanticscholar.org/paper/577e6a637f1756e115bca38726d2f6476947e02f,,semantic_scholar,,The reliability of decision-making algorithms within soft set theory is fundamentally constrained by their underlying membership functions. Traditional binary approaches overlook the implicit connections between the attributes a candidate possesses and those it lacks—connections that can be inferred
374,,A Riemannian gradient descent method for optimization on the indefinite Stiefel manifold,Dinh Van Tiep; Nguyen Thanh Son,2024,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/710dbd600ea6139bfc09db79096539f0ed2cebef,,semantic_scholar,,"We consider the optimization problem with a generally quadratic matrix constraint of the form $X^TAX = J$, where $A$ is a given nonsingular, symmetric $n\times n$ matrix and $J$ is a given $k\times k$ symmetric matrix, with $k\leq n$, satisfying $J^2 = I_k$. Since the feasible set constitutes a diff"
375,,Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering,Abdelhakim Abdellaoui; L. Benabbou; Issmail Elhallaoui,2024,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2403.14013,https://www.semanticscholar.org/paper/4f97d67b87e9214723775a5fced8ee52fd95a07e,,semantic_scholar,,Efficiently solving a vehicle routing problem (VRP) in a practical runtime is a critical challenge for delivery management companies. This paper explores both a theoretical and experimental connection between the Capacitated Vehicle Routing Problem (CVRP) and the Constrained Centroid-Based Clusterin
376,,"Network Structures, Manifold Learning and Disease Management",J. V. Ramana raju; R. Manjunatha,2025,book.anvpublication,,,,,0,0.000,0.000,10.52711/book.anv.icons-2024-006,https://www.semanticscholar.org/paper/3bec80b4f5e462cc19cff3bcb2fac545bc14f933,,semantic_scholar,,Infectious diseases continue to pose a significant global health challenge. Mathematical modeling has emerged as a critical tool for understanding disease dynamics and informing public health interventions. This paper provides a comprehensive overview of modelling infectious disease epidemics on net
377,,HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization,Zeyang Li; Kaveh Alim; Navid Azizan,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/063901ad4d5725f17dff650c854497ec3517dd38,,semantic_scholar,,"Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distributions and enabling flexible guidance at inference time. Many downstream applications, however, demand enforcing hard constraints on generated samples "
378,,A Fast Coordinate Descent Method for High-Dimensional Non-Negative Least Squares using a Unified Sparse Regression Framework,James Yang; Trevor Hastie,2024,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/23ed07fa20720ba48e197c76f4a5502dc026e871,,semantic_scholar,,"We develop theoretical results that establish a connection across various regression methods such as the non-negative least squares, bounded variable least squares, simplex constrained least squares, and lasso. In particular, we show in general that a polyhedron constrained least squares problem adm"
379,,A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent,Dai Hai Nguyen; Tetsuya Sakurai,2022,arXiv.org,,,,,2,0.000,0.000,10.1007/s10994-023-06350-9,https://www.semanticscholar.org/paper/888d3af237f3bab73eb42ab66468c5284f00c450,https://arxiv.org/pdf/2208.00587,semantic_scholar,,"We consider the optimization problem of minimizing an objective functional, which admits a variational form and is defined over probability distributions on the constrained domain, which poses challenges to both theoretical analysis and algorithmic design. Inspired by the mirror descent algorithm fo"
380,,Stable Fine-Grained Image Synthesis with Gradient-Constrained Discriminators and Hierarchical Adaptive Sampling,Duo Wang; Hengyi Wang,2025,"2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL)",,,,,0,0.000,0.000,10.1109/CVIDL65390.2025.11085797,https://www.semanticscholar.org/paper/84dc3bf16c210ab3f1b60727b6b3e2b17e65faec,,semantic_scholar,,"Recent advancements in conditional generative adversarial networks (GANs) have enabled fine-grained control over image attributes, yet critical challenges remain in long-tailed scenarios: unstable training dynamics from unbounded discriminator gradients and severe attribute entanglement caused by im"
381,,AdaProx: A Novel Method for Bilevel Optimization under Pessimistic Framework,Ziwei Guan; Daouda Sow; Sen-Fon Lin; Yingbin Liang,2025,CPAL,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/ab514fc4be46ad64390072339be6c7b623603880,,semantic_scholar,,
382,,"A Multi Affine Geometric Framework for Quantum Nonlocality. Unifying Berry Phases, Entanglement, and Coherence",Shoshauna Gauvin,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/b1ed36496f10bfda1f0e9a9d65f6a23091f55523,,semantic_scholar,,"We develop a multi affine geometric framework to unify classical and quantum mechanical laws through the lens of information geometry. By combining the principle of stationary action with maximum entropy production, we show that divergences in dual affine connections naturally give rise to quantum i"
383,,"A note on the theoretical approach to Grassmannians and Pl\""ucker coordinates for additive skew-symmetric pairwise comparisons matrices",W. W. Koczkodaj; Witold Pedrycz; A. Pigazzini; Laura P. Pigazzini; R. Pinčák,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/081a7e96b310870a7c268834ce4e4172e446b77e,,semantic_scholar,,"Symmetry and antisymmetry are fundamental concepts in many strict sciences. Pairwise comparisons (PC) matrices are fundamental tools for representing pairwise relations in decision making. In this theoretical study, we present a novel framework that embeds additive skew-symmetric PC matrices into th"
384,,Dynamic consensus-building between neocortical areas via long-range connections,Mitra Javadzadeh; Marine Schimel; Sonja B. Hofer; Yashar Ahmadian; Guillaume Hennequin,2024,bioRxiv,,,,,2,0.000,0.000,10.1101/2024.11.27.625691,https://www.semanticscholar.org/paper/95de3fc684c5f98b85f85ee341d318e32c8d3d6d,https://www.biorxiv.org/content/biorxiv/early/2024/11/27/2024.11.27.625691.full.pdf,semantic_scholar,,"The neocortex is organized into functionally specialized areas. While the functions and underlying neural circuitry of individual neocortical areas are well studied, it is unclear how these regions operate collectively to form percepts and implement cognitive processes. In particular, it remains unk"
385,,A convergence framework for energy minimisation of linear self-adjoint elliptic PDEs in nonlinear approximation spaces,Alexandre Magueresse; Santiago Badia,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2508.17687,https://www.semanticscholar.org/paper/d9289d422ab129b2553f62761d60460784e9334e,,semantic_scholar,,"Recent years have seen the emergence of nonlinear methods for solving partial differential equations (PDEs), such as physics-informed neural networks (PINNs). While these approaches often perform well in practice, their theoretical analysis remains limited, especially regarding convergence guarantee"
386,,Constrained Optimization with Compressed Gradients: A Dynamical Systems Perspective,Zhaoyue Xia; Jun Du; Chunxiao Jiang; H. V. Poor; Yong Ren,2024,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/6e94041cc04a48e3b29161476e78c9641a7cd0b5,,semantic_scholar,,"Gradient compression is of growing interests for solving constrained optimization problems including compressed sensing, noisy recovery and matrix completion under limited communication resources and storage costs. Convergence analysis of these methods from the dynamical systems viewpoint has attrac"
387,,Constrained Nonlinear Output Regulation Using Model Predictive Control,Johannes Köhler; M. Müller; F. Allgöwer,2020,IEEE Transactions on Automatic Control,,,,,28,0.000,0.000,10.1109/TAC.2021.3081080,https://www.semanticscholar.org/paper/38c1261f329dda0c799e45f450d40d8b44347f9e,https://arxiv.org/pdf/2005.12413,semantic_scholar,,We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis–Byrnes–Isidori) equations or any other offline design p
388,,Dynamic Brain Network Modeling Based on Nonlinear Low-Rank Manifold Regularization,San-Wang Wang; Zhigang Li; Kai Yuan; Shan-Shan Qu; Xin Wen,2025,IEEE Transactions on Computational Social Systems,,,,,0,0.000,0.000,10.1109/TCSS.2025.3573770,https://www.semanticscholar.org/paper/21d8ac40aa38c5f1128bfc515d5181e82faa7604,,semantic_scholar,,"Functional brain network modeling plays a crucial role in uncovering cognitive mechanisms and identifying abnormalities associated with brain disorders. However, traditional approaches—such as Pearson correlation and mutual information—typically assume that interregional relationships are static and"
389,,Complex harmonics reveal low-dimensional manifolds of critical brain dynamics,G. Deco; Y. Perl; M. Kringelbach,2024,bioRxiv,,,,,10,0.000,0.000,10.1101/2024.06.15.599165,https://www.semanticscholar.org/paper/79c92bde562ae0356acda295ead7128e8c11928b,https://www.biorxiv.org/content/biorxiv/early/2024/06/16/2024.06.15.599165.full.pdf,semantic_scholar,,"The brain needs to perform time-critical computations to ensure survival. A potential solution lies in the non-local, distributed computation at the whole-brain level made possible by criticality and amplified by the rare long-range connections found in the brain’s unique anatomical structure. This "
390,,Unfolding the Manifold Flavours of Causality,Rui A. P. Perdigão,2024,,,,,,0,0.000,0.000,10.46337/mdsc.1804,https://www.semanticscholar.org/paper/ddb8ffb2f5fd9c939b35034b7206fae818eae999,,semantic_scholar,,"The present work provides a contribution to an overarching cross-methodological causality investigation, encompassing a methodological synergy among physical, analytical, information-theoretic and systems intelligence approaches to causal discovery and quantification in complex system dynamics. Thes"
391,,Generalized Dimension Reduction Using Semi-Relaxed Gromov-Wasserstein Distance,Ranthony A. Clark; Tom Needham; Thomas Weighill,2024,AAAI Conference on Artificial Intelligence,,,,,6,0.000,0.000,10.1609/aaai.v39i15.33766,https://www.semanticscholar.org/paper/dc097cdb2866d5bb7776f54e52312b3582a81626,https://doi.org/10.1609/aaai.v39i15.33766,semantic_scholar,,"Dimension reduction techniques typically seek an embedding of a high-dimensional point cloud into a low-dimensional Euclidean space which optimally preserves the geometry of the input data. Based on expert knowledge, one may instead wish to embed the data into some other manifold or metric space in "
392,,Control and maintenance of fully-constrained and underconstrained rigid body motion on Lie groups and their tangent bundles,Brennan S. McCann; Morad Nazari,2022,The Journal of Geometric Mechanics,,,,,12,0.000,0.000,10.3934/jgm.2022002,https://www.semanticscholar.org/paper/0074129b3325b1948ecc3cc72687807e904f6270,https://www.aimsciences.org/article/exportPdf?id=eddeac28-ae6b-4159-952d-d4df118aa9cc,semantic_scholar,,"<p style='text-indent:20px;'>Presented herein are a class of methodologies for conducting constrained motion analysis of rigid bodies within the Udwadia-Kalaba (U-K) formulation. The U-K formulation, primarily devised for systems of particles, is advanced to rigid body dynamics in the geometric mech"
393,,Conjugate Priors and Posterior Inference for the Matrix Langevin Distribution on the Stiefel Manifold,Subhadip Pal; Subhajit Sengupta; Riten Mitra; Arunava Banerjee,2020,,,,,,8,0.000,0.000,10.1214/19-ba1176,https://www.semanticscholar.org/paper/e4d7be7c65fdd755b006340791547c2f9b298d4c,https://projecteuclid.org/journals/bayesian-analysis/volume-15/issue-3/Conjugate-Priors-and-Posterior-Inference-for-the-Matrix-Langevin-Distribution/10.1214/19-BA1176.pdf,semantic_scholar,,"Directional data emerges in a wide array of applications, ranging from atmospheric sciences to medical imaging. Modeling such data, however, poses unique challenges by virtue of their being constrained to non-Euclidean spaces like manifolds. Here, we present a unified Bayesian framework for inferenc"
394,,A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning,Zhehao Huang; Xinwen Cheng; Jie Zhang; Jinghao Zheng; Haoran Wang,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2505.15178,https://www.semanticscholar.org/paper/7b46064150ae7a1efed8a6baec5346e1c01d4553,,semantic_scholar,,"Recent advancements in deep models have highlighted the need for intelligent systems that combine continual learning (CL) for knowledge acquisition with machine unlearning (MU) for data removal, forming the Continual Learning-Unlearning (CLU) paradigm. While existing work treats CL and MU as separat"
395,,Optimizing Data Augmentation through Bayesian Model Selection,Madi Matymov; Ba-Hien Tran; Michael Kampffmeyer; Markus Heinonen; M. Filippone,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2505.21813,https://www.semanticscholar.org/paper/7407698af584a2073234e6812e215588d3d656c3,,semantic_scholar,,"Data Augmentation (DA) has become an essential tool to improve robustness and generalization of modern machine learning. However, when deciding on DA strategies it is critical to choose parameters carefully, and this can be a daunting task which is traditionally left to trial-and-error or expensive "
396,,Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints,Pekka Malo; L. Viitasaari; Antti-Jussi Suominen; Eeva Vilkkumaa; O. Tahvonen,2024,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2411.19193,https://www.semanticscholar.org/paper/7ba834ca2bbfebec6b7da4803b68a731873e991e,,semantic_scholar,,"This paper examines reinforcement learning (RL) in infinite-horizon decision processes with almost-sure safety constraints, crucial for applications like autonomous systems, finance, and resource management. We propose a doubly-regularized RL framework combining reward and parameter regularization t"
397,,Graph Neural Diffusion via Generalized Opinion Dynamics,Asela Hevapathige; Asiri Wijesinghe; Ahad N. Zehmakan,2025,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2508.11249,https://www.semanticscholar.org/paper/546229d0837023e8f183cdc4a3dcded208bd3382,,semantic_scholar,,"There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical diffusion processes. However, existing methods suffer from three critical limitations: (1) they rely on homogeneous diffus"
398,,cryoSENSE: Compressive Sensing Enables High-throughput Microscopy with Sparse and Generative Priors on the Protein Cryo-EM Image Manifold,Zain Shabeeb; Daniel Saeedi; Darin Tsui; Vida Jamali; Amirali Aghazadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.12931v2,https://arxiv.org/pdf/2511.12931v2,arxiv,,"Cryo-electron microscopy (cryo-EM) enables the atomic-resolution visualization of biomolecules; however, modern direct detectors generate data volumes that far exceed the available storage and transfer bandwidth, thereby constraining practical throughput. We introduce cryoSENSE, the computational re"
399,,Channel-Constrained Markovian Quantum Diffusion Model from Open System Perspective,Qin-Sheng Zhu; Geng Chen; Lian-Hui Yu; Xiaodong Xing; Xiao-Yu Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.12221v1,https://arxiv.org/pdf/2511.12221v1,arxiv,,"We present a channel-constrained Markovian quantum diffusion (CCMQD) model that prepares quantum states by rigorously framing the generative process within the dynamics of open quantum systems. Our model interprets the forward diffusion process as natural decoherence using quantum master equations, "
400,,Formation of Close Binaries through Massive Black Hole Perturbations and Chaotic Tides,Howard Hao-Tse Huang; Wenbin Lu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.11965v1,https://arxiv.org/pdf/2511.11965v1,arxiv,,Hills breakup of binary systems allows massive black holes (MBH) to produce hyper-velocity stars (HVSs) and tightly bound stars. The long timescale of orbital relaxation means that binaries must spend numerous orbits around the MBH before they are tidally broken apart. Repeated MBH tidal perturbatio
401,,Reaching for the Edge II: Stellar Halos out to Large Radii as a Tracer of Dark Matter Halo Mass,Katya Leidig; Benedikt Diemer; Song Huang; Shuo Xu; Conghao Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.10723v1,https://arxiv.org/pdf/2511.10723v1,arxiv,,"The diffuse outskirts of brightest cluster galaxies (BCGs) encode valuable information about the assembly history and mass of their host dark matter halos. However, the low surface brightness of these stellar halos has historically made them difficult to observe. Recent deep imaging, particularly wi"
402,,Deformations of Locally Conformal Spin(7) Instantons,Eyup Yalcinkaya,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.09161v1,https://arxiv.org/pdf/2511.09161v1,arxiv,,"We explore the deformation theory of instantons on locally conformal (LC) $Spin(7)$ manifolds. These structures, characterized by a non-parallel fundamental 4-form $Φ$ satisfying $dΦ= θ\wedge Φ$, represent a significant, yet geometrically constrained, class of non-integrable $G$-structures. We analy"
403,,Tube Integrability in a Time-Dependent Nonlinear Oscillator,Johannes Hagel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.13740v1,https://arxiv.org/pdf/2511.13740v1,arxiv,,"We study the nonlinear oscillator z'' + omega^2 z + g(t) z^2 = 0 with a time-dependent coefficient g(t). We show that this equation admits an exact quadratic invariant I(z,p,t) provided that g(t) = alpha2(t)^(-5/2) and that alpha2(t) satisfies a nonlinear third-order differential equation. The resul"
404,,Contact Wasserstein Geodesics for Non-Conservative Schrödinger Bridges,Andrea Testa; Søren Hauberg; Tamim Asfour; Leonel Rozo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.06856v2,https://arxiv.org/pdf/2511.06856v2,arxiv,,"The Schrödinger Bridge provides a principled framework for modeling stochastic processes between distributions; however, existing methods are limited by energy-conservation assumptions, which constrains the bridge's shape preventing it from model varying-energy phenomena. To overcome this, we introd"
405,,Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings,Brian B. Avants; Nicholas J. Tustison; James R Stone,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.06425v1,https://arxiv.org/pdf/2511.06425v1,arxiv,,"Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting th"
406,,Geometrically robust least squares through manifold optimization,Jeremy Coulson; Alberto Padoan; Cyrus Mostajeran,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.03644v1,https://arxiv.org/pdf/2511.03644v1,arxiv,,"This paper presents a methodology for solving a geometrically robust least squares problem, which arises in various applications where the model is subject to geometric constraints. The problem is formulated as a minimax optimization problem on a product manifold, where one variable is constrained t"
407,,Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning,Qingyi Chen; Ruiqi Ni; Jun Kim; Ahmed H. Qureshi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.03591v1,https://arxiv.org/pdf/2511.03591v1,arxiv,,"Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environments while adhering to manifold constraints imposed by tasks. For example, service robots must carry cups upright while avo"
408,,SKGE: Spherical Knowledge Graph Embedding with Geometric Regularization,Xuan-Truong Quan; Xuan-Son Quan; Duc Do Minh; Vinh Nguyen Van,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.02460v1,https://arxiv.org/pdf/2511.02460v1,arxiv,,"Knowledge graph embedding (KGE) has become a fundamental technique for representation learning on multi-relational data. Many seminal models, such as TransE, operate in an unbounded Euclidean space, which presents inherent limitations in modeling complex relations and can lead to inefficient trainin"
409,,SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation,Yufeng Jin; Niklas Funk; Vignesh Prasad; Zechu Li; Mathias Franzius,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.01501v1,https://arxiv.org/pdf/2511.01501v1,arxiv,,"Object pose estimation is a fundamental problem in robotics and computer vision, yet it remains challenging due to partial observability, occlusions, and object symmetries, which inevitably lead to pose ambiguity and multiple hypotheses consistent with the same observation. While deterministic deep "
410,,The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold,Tiberiu Musat,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2511.01938v1,https://arxiv.org/pdf/2511.01938v1,arxiv,,"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 u"
411,,Robust Graph Condensation via Classification Complexity Mitigation,Jiayi Luo; Qingyun Sun; Beining Yang; Haonan Yuan; Xingcheng Fu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.26451v2,https://arxiv.org/pdf/2510.26451v2,arxiv,,"Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates s"
412,,Causal-Aware Generative Adversarial Networks with Reinforcement Learning,Tu Anh Hoang Nguyen; Dang Nguyen; Tri-Nhan Vo; Thuc Duy Le; Sunil Gupta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.24046v1,https://arxiv.org/pdf/2510.24046v1,arxiv,,"The utility of tabular data for tasks ranging from model training to large-scale data analysis is often constrained by privacy concerns or regulatory hurdles. While existing data generation methods, particularly those based on Generative Adversarial Networks (GANs), have shown promise, they frequent"
413,,"The Gravitational Aspect of Information: The Physical Reality of Asymmetric ""Distance""",Tomoi Koide; Armin van de Venn,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.22664v3,https://arxiv.org/pdf/2510.22664v3,arxiv,,"We show that when a Brownian bridge is physically constrained to satisfy a canonical condition, its time evolution exactly coincides with an m-geodesic on the statistical manifold of Gaussian distributions. This identification provides a direct physical realization of a geometric concept in informat"
414,,"If You Want to Be Robust, Be Wary of Initialization",Sofiane Ennadir; Johannes F. Lutzeyer; Michalis Vazirgiannis; El Houcine Bergou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.22652v1,https://arxiv.org/pdf/2510.22652v1,arxiv,,"Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversarial perturbations. While prevailing defense strategies focus primarily on pre-processing techniques and adaptive message-p"
415,,Confidence is Not Competence,Debdeep Sanyal; Manya Pandey; Dhruv Kumar; Saurabh Deshpande; Murari Mandal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.24772v1,https://arxiv.org/pdf/2510.24772v1,arxiv,,Large language models (LLMs) often exhibit a puzzling disconnect between their asserted confidence and actual problem-solving competence. We offer a mechanistic account of this decoupling by analyzing the geometry of internal states across two phases - pre-generative assessment and solution executio
416,,Landau Polarons as Generators of Quantum-Coherent States,Arnab Ghosh; Patrick Brosseau; Dmitry N. Dirin; Rui Tao; Maksym V. Kovalenko,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.20962v1,https://arxiv.org/pdf/2510.20962v1,arxiv,,"Since Landau's theory, polarons have been understood as quasiparticles in which charges are dressed by the lattice field, yet decades of transport and spectroscopic studies have yielded only static indirect renormalizations. Whether such dressing can dynamically reorganize electronic spectra to gene"
417,,Bayesian Prediction under Moment Conditioning,Nicholas G. Polson; Daniel Zantedeschi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.20742v1,https://arxiv.org/pdf/2510.20742v1,arxiv,,"Prediction is a central task of statistics and machine learning, yet many inferential settings provide only partial information, typically in the form of moment constraints or estimating equations. We develop a finite, fully Bayesian framework for propagating such partial information through predict"
418,,PDE-Free Mass-Constrained Learning of Complex Systems with Hidden States: The crowd dynamics case,Gianmaria Viola; Alessandro Della Pia; Lucia Russo; Ioannis Kevrekidis; Constantinos Siettos,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.17657v2,https://arxiv.org/pdf/2510.17657v2,arxiv,,"We propose a machine learning framework based on the next-generation Equation-Free algorithm for learning the spatio-temporal dynamics of mass-constrained complex systems with hidden states, whose dynamics can in principle be described by PDEs, but lack explicit models. In these cases, some variable"
419,,3DPR: Single Image 3D Portrait Relight using Generative Priors,Pramod Rao; Abhimitra Meka; Xilong Zhou; Gereon Fox; Mallikarjun B R,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.15846v1,https://arxiv.org/pdf/2510.15846v1,arxiv,,"Rendering novel, relit views of a human head, given a monocular portrait image as input, is an inherently underconstrained problem. The traditional graphics solution is to explicitly decompose the input image into geometry, material and lighting via differentiable rendering; but this is constrained "
420,,Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic,Jianguo Chen; Zhengqin Liu; Jinlong Lei; Peng Yi; Yiguang Hong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.15573v1,https://arxiv.org/pdf/2510.15573v1,arxiv,,"With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the "
421,,Riemannian Bilevel Optimization with Gradient Aggregation,Zhuo Chen; Xinjian Xu; Shihui Ying; Tieyong Zeng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.15305v1,https://arxiv.org/pdf/2510.15305v1,arxiv,,"Bilevel optimization (BLO) offers a principled framework for hierarchical decision-making and has been widely applied in machine learning tasks such as hyperparameter optimization and meta-learning. While existing BLO methods are mostly developed in Euclidean spaces, many real-world problems involve"
422,,Learning an Image Editing Model without Image Editing Pairs,Nupur Kumari; Sheng-Yu Wang; Nanxuan Zhao; Yotam Nitzan; Yuheng Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.14978v1,https://arxiv.org/pdf/2510.14978v1,arxiv,,"Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of input-target pairs. This is a critical bottleneck, as such naturally occurring pairs are hard to curate at scale. Current"
423,,X-ray panorama of the SS433/W50 complex by SRG/eROSITA,Rashid Sunyaev; Ildar Khabibullin; Eugene Churazov; Marat Gilfanov; Pavel Medvedev,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.14938v1,https://arxiv.org/pdf/2510.14938v1,arxiv,,"Galactic microquasar SS433 and the radio nebula W50 surrounding it present a prototypical example of a hyper-Eddington binary system shaping its ambient interstellar medium via energetic outflows. In this paper, we present X-ray observations of the SS433/W50 complex by the eROSITA telescope onboard "
424,,TED++: Submanifold-Aware Backdoor Detection via Layerwise Tubular-Neighbourhood Screening,Nam Le; Leo Yu Zhang; Kewen Liao; Shirui Pan; Wei Luo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.14299v1,https://arxiv.org/pdf/2510.14299v1,arxiv,,"As deep neural networks power increasingly critical applications, stealthy backdoor attacks, where poisoned training inputs trigger malicious model behaviour while appearing benign, pose a severe security risk. Many existing defences are vulnerable when attackers exploit subtle distance-based anomal"
425,,When Flatness Does (Not) Guarantee Adversarial Robustness,Nils Philipp Walter; Linara Adilova; Jilles Vreeken; Michael Kamp,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.14231v1,https://arxiv.org/pdf/2510.14231v1,arxiv,,"Despite their empirical success, neural networks remain vulnerable to small, adversarial perturbations. A longstanding hypothesis suggests that flat minima, regions of low curvature in the loss landscape, offer increased robustness. While intuitive, this connection has remained largely informal and "
426,,Manifold Decoders: A Framework for Generative Modeling from Nonlinear Embeddings,Riddhish Thakare; Kingdom Mutala Akugri,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.13622v1,https://arxiv.org/pdf/2510.13622v1,arxiv,,"Classical nonlinear dimensionality reduction (NLDR) techniques like t-SNE, Isomap, and LLE excel at creating low-dimensional embeddings for data visualization but fundamentally lack the ability to map these embeddings back to the original high-dimensional space. This one-way transformation limits th"
427,,Toward Hyper-Dimensional Connectivity in Beyond 6G: A Conceptual Framework,Ekram Hossain; Angelo Vera-Rivera,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.12896v1,https://arxiv.org/pdf/2510.12896v1,arxiv,,"Cellular wireless networks enable mobile broadband connectivity for Internet-based applications through their radio access and core network infrastructure. While Fifth-Generation (5G) cellular systems are currently being deployed, ongoing research on cellular technologies primarily focuses on Sixth-"
428,,Cautious Weight Decay,Lizhang Chen; Jonathan Li; Kaizhao Liang; Baiyu Su; Cong Xie,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.12402v1,https://arxiv.org/pdf/2510.12402v1,arxiv,,"We introduce Cautious Weight Decay (CWD), a one-line, optimizer-agnostic modification that applies weight decay only to parameter coordinates whose signs align with the optimizer update. Unlike standard decoupled decay, which implicitly optimizes a regularized or constrained objective, CWD preserves"
429,,Non-Hermitian Realization of Quantum Dynamics on Embedded Manifolds,Samuel Alperin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.11845v1,https://arxiv.org/pdf/2510.11845v1,arxiv,,"We show that the Floquet Hamiltonian of a quantum particle driven by a general time-periodic imaginary potential is exactly equivalent, at stroboscopic times, to the Hamiltonian of a free particle constrained to a curved Riemannian manifold with fixed embedding. We illustrate the construction for a "
430,,Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars,Rodrigo Córdova Rosado; Andy D. Goulding; Jenny E. Greene; Nickolas Kokron; Andrina Nicola,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.11780v1,https://arxiv.org/pdf/2510.11780v1,arxiv,,Understanding the dark matter (DM) halo environment in which galaxies that host active galactic nuclei (AGN) reside is a window into the nature of supermassive black hole (SMBH) accretion. We apply halo occupation distribution (HOD) modeling tools to interpret the angular cross-correlation functions
431,,Renormalization of Interacting Random Graph Models,Alessio Catanzaro; Diego Garlaschelli; Subodh P. Patil,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.07186v2,https://arxiv.org/pdf/2510.07186v2,arxiv,,"Random graphs offer a useful mathematical representation of a variety of real world complex networks. Exponential random graphs, for example, are particularly suited towards generating random graphs constrained to have specified statistical moments. In this investigation, we elaborate on a generaliz"
432,,Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs,David Boetius; Abdelrahman Abdelnaby; Ashok Kumar; Stefan Leue; Abdalla Swikir,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.05707v1,https://arxiv.org/pdf/2510.05707v1,arxiv,,"Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on Riemannian manifolds poses significant challenges due to the manifold's geometric constraints. To address this, we propos"
433,,From News to Returns: A Granger-Causal Hypergraph Transformer on the Sphere,Anoushka Harit; Zhongtian Sun; Jongmin Yu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.04357v1,https://arxiv.org/pdf/2510.04357v1,arxiv,,"We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional infl"
434,,Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit,Peiwen Yang; Weisong Wen; Runqiu Yang; Yuanyuan Zhang; Jiahao Hu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.04278v1,https://arxiv.org/pdf/2510.04278v1,arxiv,,"Model predictive control (MPC) faces significant limitations when applied to systems evolving on nonlinear manifolds, such as robotic attitude dynamics and constrained motion planning, where traditional Euclidean formulations struggle with singularities, over-parameterization, and poor convergence. "
435,,Asymmetric rational reductions of 2D-Toda hierarchy and a generalized Frobenius manifold,Haonan Qu; Qiulan Zhao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.04151v1,https://arxiv.org/pdf/2510.04151v1,arxiv,,"We study the local bihamiltonian structures of the asymmetric rational reductions of the 2D-Toda hierarchy (RR2T) of types $(2,1)$ and $(1,2)$ at the full-dispersive level, and construct a three-dimensional generalized Frobenius manifold with non-flat unity associated with the $(2,1)$-type. Furtherm"
436,,Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets,Muhao Guo; Haoran Li; Yang Weng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.04138v1,https://arxiv.org/pdf/2510.04138v1,arxiv,,"Neural ordinary differential equations (NODE) have garnered significant attention for their design of continuous-depth neural networks and the ability to learn data/feature dynamics. However, for high-dimensional systems, estimating dynamics requires extensive calculations and suffers from high trun"
437,,The Principle of Isomorphism: A Theory of Population Activity in Grid Cells and Beyond,Maoshen Xu; Fei Song; Yuxiu Shao; Bailu Si; Shanshan Qin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.02853v2,https://arxiv.org/pdf/2510.02853v2,arxiv,,"Identifying the principles that determine neural population activity is paramount in the field of neuroscience. We propose the Principle of Isomorphism (PIso): population activity preserves the essential mathematical structures of the tasks it supports. Using grid cells as a model system, we show th"
438,,Action Deviation-Aware Inference for Low-Latency Wireless Robots,Jeyoung Park; Yeonsub Lim; Seungeun Oh; Jihong Park; Jinho Choi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.02851v2,https://arxiv.org/pdf/2510.02851v2,arxiv,,"To support latency-sensitive AI applications ranging from autonomous driving to industrial robot manipulation, 6G envisions distributed ML with computational resources in mobile, edge, and cloud connected over hyper-reliable low-latency communication (HRLLC). In this setting, speculative decoding ca"
439,,Topological Invariance and Breakdown in Learning,Yongyi Yang; Tomaso Poggio; Isaac Chuang; Liu Ziyin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.02670v1,https://arxiv.org/pdf/2510.02670v1,arxiv,,"We prove that for a broad class of permutation-equivariant learning rules (including SGD, Adam, and others), the training process induces a bi-Lipschitz mapping between neurons and strongly constrains the topology of the neuron distribution during training. This result reveals a qualitative differen"
440,,StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold,Zhizhong Li; Sina Sajadmanesh; Jingtao Li; Lingjuan Lyu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2510.01938v1,https://arxiv.org/pdf/2510.01938v1,arxiv,,"Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning in performance, partly due to its insufficient exploitation of the geometric structure underlying low-rank manifolds. I"
441,,Sparse view tomographic reconstruction of elongated objects using learned primal-dual networks,Buda Bajić; Johannes A. J. Huber; Benedikt Neyses; Linus Olofsson; Ozan Öktem,2025,Engineering Applications of Artificial Intelligence,,,,,0,0.000,0.000,10.1016/j.engappai.2025.112295,https://openalex.org/W4392538367,https://doi.org/10.1016/j.engappai.2025.112295,openalex,,
442,,S2PW-Mamba: Pinwheel and Wavelet-based Spatial-Spectral Mamba for Hyperspectral Image Classification,Lianhui Liang; Wangli He; Ying Zhang; Y. J. Zeng; Thomas Wu,2025,,,,,,0,0.000,0.000,10.36227/techrxiv.175693604.45516380/v1,https://openalex.org/W4413966806,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175693604.45516380/v1,openalex,,
443,,Introduction,Yidong Xu; Jianghong Mao; Weijie Zhuge; Xiaoniu Yu; Ping Wu,2025,,,,,,0,0.000,0.000,10.1007/978-981-96-8237-9_1,https://openalex.org/W4413860807,https://link.springer.com/content/pdf/10.1007/978-981-96-8237-9_1.pdf,openalex,,
444,,SparseFraudNet: A Graph-based Approach for Cold-start Fraud Detection with Information Aggregation,Wen Zhang; Rui Li; Quan Bai; Song Wang,2025,ACM Transactions on Information Systems,,,,,0,0.000,0.000,10.1145/3748719,https://openalex.org/W4413418666,https://dl.acm.org/doi/pdf/10.1145/3748719,openalex,,"Online reviews play a critical role in influencing consumer’s purchasing decision on e-commerce, making them a prime target for manipulation through fraudulent reviews. Although various Fraud Detection (FD) techniques have been presented, a crucial problem still remains unaddressed, i.e., the cold-s"
445,,Hyperbolic Deep Learning for Foundation Models: A Survey,Neil He; Hiren Madhu; Ngoc Bui; Meng‐Lin Yang; Rex Ying,2025,,,,,,0,0.000,0.000,10.1145/3711896.3736564,https://openalex.org/W4412875482,https://dl.acm.org/doi/pdf/10.1145/3711896.3736564,openalex,,"Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable success in diverse downstream tasks. However, recent studies have shown fundamental limitations of these models: (1) lim"
446,,OptWake-YOLO: a lightweight and efficient ship wake detection model based on optical remote sensing images,Robert C. Qiu; Nan Bi,2025,Frontiers in Marine Science,,,,,1,0.000,0.000,10.3389/fmars.2025.1624323,https://openalex.org/W4412821756,https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1624323/pdf,openalex,,"Introduction Ship wakes exhibit more distinctive characteristics than vessels themselves, making wake detection more feasible than direct ship detection. However, challenges persist due to sea surface interference, meteorological conditions, and coastal structures, while practical applications deman"
447,,A Method for Multimodal Remote Sensing Image Classification,Zhong Sun; Bin Hu,2025,Journal of Organizational and End User Computing,,,,,0,0.000,0.000,10.4018/joeuc.384397,https://openalex.org/W4412083045,https://www.igi-global.com/ViewTitle.aspx?TitleId=384397&isxn=9798337311579,openalex,,"In remote sensing, images are widely used in applications, such as land cover classification, urban monitoring, and disaster management, providing rich information about the Earth's surface. However, due to data heterogeneity and scarcity, different modalities of remote-sensing images often face cha"
448,,Ensemble Kalman methods: A mean-field perspective,Edoardo Calvello; Stephanie Reich; Andrew M. Stuart,2025,Acta Numerica,,,,,6,0.000,0.000,10.1017/s0962492924000060,https://openalex.org/W4411917706,https://www.cambridge.org/core/services/aop-cambridge-core/content/view/94C9B874BBD4F11B8D36DD42D9F01BC7/S0962492924000060a.pdf/div-class-title-ensemble-kalman-methods-a-mean-field-perspective-div.pdf,openalex,,"Ensemble Kalman methods, introduced in 1994 in the context of ocean state estimation, are now widely used for state estimation and parameter estimation (inverse problems) in many arenae. Their success stems from the fact that they take an underlying computational model as a black box to provide a sy"
449,,Talks,,2025,FEBS Open Bio,,,,,0,0.000,0.000,10.1002/2211-5463.70069,https://openalex.org/W4412764722,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/2211-5463.70069,openalex,,
450,,Destructive Creation of New Invasive Technologies: Generative Artificial Intelligence Behaviour,Mario Coccia,2025,Technologies,,,,,1,0.000,0.000,10.3390/technologies13070261,https://openalex.org/W4411495197,https://www.mdpi.com/2227-7080/13/7/261/pdf?version=1750412465,openalex,,This study proposes a new concept that explains a source of technological change: The invasive behaviour of general purpose technologies that breaks into scientific and technological ecosystems with accelerated diffusion of new products and processes that destroy the usage value of all units previou
451,,Nonlinear Dynamics in Game Theory as a New Mathematical Approach to Analysing Strategic Behaviour,Abiodun Finbarrs Oketunji,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202506.1702.v1,https://openalex.org/W4411528337,https://www.preprints.org/frontend/manuscript/5447ba31dca22ca010cb741da18287e1/download_pub,openalex,,"This research presents a novel mathematical framework integrating nonlinear dynamics with game theory to analyse strategic behaviour in complex multi-agent systems. Traditional game-theoretic approaches often assume equilibrium convergence and rational decision-making, yet empirical observations rev"
452,,sHGCN: Simplified hyperbolic graph convolutional neural networks,Paredes Arévalo; Alexis Molina; Álvaro Ciudad,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2506.14438,https://openalex.org/W4415311858,https://arxiv.org/pdf/2506.14438,openalex,,"Hyperbolic geometry has emerged as a powerful tool for modeling complex, structured data, particularly where hierarchical or tree-like relationships are present. By enabling embeddings with lower distortion, hyperbolic neural networks offer promising alternatives to Euclidean-based models for captur"
453,,"The Past, Present and Future of the Corporate Actor: Ontological, Epistemological and Theoretical Considerations",Michaela Haase; Elke Schuessler; Ute Schmiel; Günther Ortmann; Andreas Suchanek,2025,Schmalenbach Journal of Business Research,,,,,1,0.000,0.000,10.1007/s41471-025-00213-w,https://openalex.org/W4411342480,https://link.springer.com/content/pdf/10.1007/s41471-025-00213-w.pdf,openalex,,"Abstract Corporate actors are more resourceful, more powerful and more capable of influencing their own conditions of action than most other actors. This curated article argues that to imagine the future of corporate actors in a world that is rapidly changing due to the possibilities of digital tech"
454,,Fault-Tolerant Logical Measurements via Homological Measurement,Benjamin Ide; Manoj G. Gowda; Priya J. Nadkarni; Guillaume Dauphinais,2025,Physical Review X,,,,,0,0.000,0.000,10.1103/physrevx.15.021088,https://openalex.org/W4403884303,http://link.aps.org/pdf/10.1103/PhysRevX.15.021088,openalex,,"We introduce homological measurement, a framework for measuring the logical Pauli operators encoded in Calderbank-Shor-Steane stabilizer codes. The framework is based on the algebraic description of such codes as chain complexes. Protocols such as lattice surgery and some of its recent generalizatio"
455,,Artificial Intelligence Across Borders: Transforming Industries Through Intelligent Innovation,,2025,,,,,,2,0.000,0.000,10.70593/978-93-49910-25-6,https://openalex.org/W4411150579,https://deepscienceresearch.com/dsr/catalog/download/156/832/1717,openalex,,
456,,Algorithm- and Data-Dependent Generalization Bounds for Score-Based Generative Models,Benjamin Dupuis; Dario Shariatian; Maxime Haddouche; Alain Durmus; Umut Şimşekli,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2506.03849,https://openalex.org/W4416073882,https://arxiv.org/pdf/2506.03849,openalex,,"Score-based generative models (SGMs) have emerged as one of the most popular classes of generative models. A substantial body of work now exists on the analysis of SGMs, focusing either on discretization aspects or on their statistical performance. In the latter case, bounds have been derived, under"
457,,Vector Ising spin annealer for minimizing Ising Hamiltonians,James Cummins; Natalia G. Berloff,2025,Communications Physics,,,,,2,0.000,0.000,10.1038/s42005-025-02145-7,https://openalex.org/W4410855184,https://www.nature.com/articles/s42005-025-02145-7.pdf,openalex,,"Abstract Complex optimization problems can be solved via dedicated machines which encode the problem in the couplings of spin Hamiltonians. However, traditional physical minimizers often select excited states due to limitations in spin dynamics. We introduce the Vector Ising Spin Annealer (VISA), a "
458,,Optimal Rotational Smoothing on S1: Why Only the Poisson Kernel Survives on the Circle,D. R. Stanley,2025,,,,,,0,0.000,0.000,10.31219/osf.io/a4gvz_v1,https://openalex.org/W4410807039,https://osf.io/a4gvz_v1/download,openalex,,"Circular signals—angles, phases, orientations—pervade modern science and engineering yet resist straightforward linear smoothing techniques.Context. Circular data arise in disciplines ranging from wind forecasting to phase-unwrapping and cryo-EM.Problem. Despite a century of practice, no consensus e"
459,,SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time,Zhening Huang; Hyeonho Jeong; Xuelin Chen; Yulia Gryaditskaya; Tuanfeng Y. Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25075v1,https://arxiv.org/pdf/2512.25075v1,arxiv,,"We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous"
460,,Scaling Open-Ended Reasoning to Predict the Future,Nikhil Chandak; Shashwat Goel; Ameya Prabhu; Moritz Hardt; Jonas Geiping,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25070v1,https://arxiv.org/pdf/2512.25070v1,arxiv,,"High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a f"
461,,Many Minds from One Model: Bayesian Transformers for Population Intelligence,Diji Yang; Yi Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25063v1,https://arxiv.org/pdf/2512.25063v1,arxiv,,"Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we prop"
462,,Melting curve of correlated iron at Earth's core conditions from machine-learned DFT+DMFT,Rishi Rao; Li Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25061v1,https://arxiv.org/pdf/2512.25061v1,arxiv,,"Reliable constraints on iron's melting curve at Earth's inner-core boundary require accurate finite-temperature electronic correlations, yet DFT+DMFT calculations remain too costly for large-scale thermodynamic sampling. Here, we develop a machine-learning accelerator for charge self-consistent DFT+"
463,,On the geometry and topology of representations: the manifolds of modular addition,Gabriela Moisescu-Pareja; Gavin McCracken; Harley Wiltzer; Vincent Létourneau; Colin Daniels,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25060v1,https://arxiv.org/pdf/2512.25060v1,arxiv,,"The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both unifor"
464,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25059v1,https://arxiv.org/pdf/2512.25059v1,arxiv,,"Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training"
465,,Fluid dynamics as intersection problem,Nikita Nekrasov; Paul Wiegmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25053v1,https://arxiv.org/pdf/2512.25053v1,arxiv,,"We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, a"
466,,Bilinear tau forms of quantum Painlevé equations and $\mathbb{C}^2/\mathbb{Z}_2$ blowup relations in SUSY gauge theories,Giulio Bonelli; Anton Shchechkin; Alessandro Tanzini,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25051v1,https://arxiv.org/pdf/2512.25051v1,arxiv,,"We derive bilinear tau forms of the canonically quantized Painlevé equations, thereby relating them to those previously obtained from the $\mathbb{C}^2/\mathbb{Z}_2$ blowup relations for the $\mathcal{N}=2$ supersymmetric gauge theory partition functions on a general $Ω$-background. We fully fix the"
467,,Arithmetic with spatiotemporal optical vortex of integer and fractional topological charges,Hsiao-Chih Huang; Chen-Ting Liao; Hui Min Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25049v1,https://arxiv.org/pdf/2512.25049v1,arxiv,,"Spatiotemporal optical vortices carry transverse orbital angular momentum (t-OAM), which give rise to spatiotemporal topological charge (ST-TC). To unleash the full potential of t-OAM in expanding the capacity of communication and computing, we demonstrate the first optical information-processing pi"
468,,Generative Classifiers Avoid Shortcut Solutions,Alexander C. Li; Ananya Kumar; Deepak Pathak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25034v1,https://arxiv.org/pdf/2512.25034v1,arxiv,,"Discriminative approaches to classification often learn shortcuts that hold in-distribution but fail even under minor distribution shift. This failure mode stems from an overreliance on features that are spuriously correlated with the label. We show that generative classifiers, which use class-condi"
469,,Testing Monotonicity in a Finite Population,Jiafeng Chen; Jonathan Roth; Jann Spiess,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25032v1,https://arxiv.org/pdf/2512.25032v1,arxiv,,"We consider the extent to which we can learn from a completely randomized experiment whether everyone has treatment effects that are weakly of the same sign, a condition we call monotonicity. From a classical sampling perspective, it is well-known that monotonicity is untestable. By contrast, we sho"
470,,On Nonlinear Inertial Transformations,Nicholas Agia,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25024v1,https://arxiv.org/pdf/2512.25024v1,arxiv,,"It is often assumed that the most general transformation between two inertial reference frames is affine linear in their Cartesian coordinates, an assumption which is however not true. We provide a complete derivation of the most general inertial frame transformation, which is indeed nonlinear; alon"
471,,ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning,Timo Kaufmann; Yannick Metz; Daniel Keim; Eyke Hüllermeier,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25023v1,https://arxiv.org/pdf/2512.25023v1,arxiv,,"Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas over grapes, but which preference is stronger? Strength is crucial for decision-making under uncertainty and generalizat"
472,,"Detector Response Matrices, Effective Areas, and Flash-Effective Areas for Radiation Detectors",Gregory Bowers; Eve Chase; William Ford; Daniel Coupland; Brian Larsen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25021v1,https://arxiv.org/pdf/2512.25021v1,arxiv,,"A Detector Response Matrix (DRM) is a discrete representation of an instrument's Detector Response Function (DRF), which quantifies how many discrete energy depositions occur in a detector volume for a given distribution of particles incident on the detector. For simple radiation detectors that can "
473,,Convergence of the generalization error for deep gradient flow methods for PDEs,Chenguang Liu; Antonis Papapantoleon; Jasper Rou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25017v1,https://arxiv.org/pdf/2512.25017v1,arxiv,,The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We f
474,,A note on semistable unitary operators on $L^2(\mathbb{R})$,Xianghong Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25013v1,https://arxiv.org/pdf/2512.25013v1,arxiv,,"In this note, we present a characterization of semistable unitary operators on $L^2(\mathbb{R})$, under the assumption that the operator is (i) translation-invariant, (ii) symmetric, and (iii) locally uniformly continuous (LUC) under dilation. As a consequence, we characterize one-parameter groups f"
475,,FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM,Yuchen Wu; Jiahe Li; Fabio Tosi; Matteo Poggi; Jin Zheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25008v1,https://arxiv.org/pdf/2512.25008v1,arxiv,,"We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robust tracking and mapping. Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance f"
476,,Grassmannian Geometries for Non-Planar On-Shell Diagrams,Artyom Lisitsyn; Umut Oktem; Melissa Sherman-Bennett; Jaroslav Trnka,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25005v1,https://arxiv.org/pdf/2512.25005v1,arxiv,,"On-shell diagrams are gauge invariant quantities which play an important role in the description of scattering amplitudes. Based on the principles of generalized unitarity, they are given by products of elementary three-point amplitudes where the kinematics of internal on-shell legs are determined b"
477,,Efficiently Estimating Data Efficiency for Language Model Fine-tuning,Gyung Hyun Je; Colin Raffel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24991v1,https://arxiv.org/pdf/2512.24991v1,arxiv,,"While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is"
478,,"Wall crossing, string networks and quantum toroidal algebras",Yegor Zenkevich,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24988v1,https://arxiv.org/pdf/2512.24988v1,arxiv,,"We investigate BPS states in 4d N=4 supersymmetric Yang-Mills theory and the corresponding (p, q) string networks in Type IIB string theory. We propose a new interpretation of the algebra of line operators in this theory as a tensor product of vector representations of a quantum toroidal algebra, wh"
479,,Local approximations of global Hamiltonian from inclusion of algebras,Yidong Chen; Nima Lashkari; Kwing Lam Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25062v1,https://arxiv.org/pdf/2512.25062v1,arxiv,,"We write down the global Hamiltonian of conformal field theory (CFT) in finite volume in terms of the modular Hamiltonian of the vacuum reduced to a local ball-shaped region, and use it to propose local approximations to the global Minkowski Hamiltonian in quantum field theory (QFT). The proposed Ha"
480,,The variety of orthogonal frames,Laura Casabella; Alessio Sammartano,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25058v1,https://arxiv.org/pdf/2512.25058v1,arxiv,,"An orthogonal n-frame is an ordered set of n pairwise orthogonal vectors. The set of all orthogonal n-frames in a d-dimensional quadratic vector space is an algebraic variety V(d,n). In this paper, we investigate the variety V(d,n) as well as the quadratic ideal I(d,n) generated by the orthogonality"
481,,Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere,Yin Tang; Cristian Voinea; Liangdong Hu; Zlatko Papić; W. Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25054v1,https://arxiv.org/pdf/2512.25054v1,arxiv,,"Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising criti"
482,,Amplitude constraints on dark energy,Scott Melville,2025,arXiv,,,,,0,0.000,0.000,10.58027/3q8k-ew90,http://arxiv.org/abs/2512.25047v1,https://arxiv.org/pdf/2512.25047v1,arxiv,,"This talk gives a short introduction to the ``UV/EFT correspondence"", which uses scattering amplitudes to relate the Effective Field Theory (EFT) coefficients probed by low-energy measurements to properties of the underlying high-energy (UV) completion. This includes recent ``positivity bounds"" on E"
483,,The Hochschild homology of a noncommutative symmetric quotient stack,Rina Anno; Vladimir Baranovsky; Timothy Logvinenko,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25039v1,https://arxiv.org/pdf/2512.25039v1,arxiv,,"We prove an orbifold type decomposition theorem for the Hochschild homology of the symmetric powers of a small DG category $\mathcal{A}$. In noncommutative geometry, these can be viewed as the noncommutative symmetric quotient stacks of $\mathcal{A}$. We use this decomposition to show that the total"
484,,Anomalous (3+1)d Fermionic Topological Quantum Field Theories via Symmetry Extension,Zheyan Wan; Juven Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25038v1,https://arxiv.org/pdf/2512.25038v1,arxiv,,"Discrete finite-group global symmetries may suffer from nonperturbative 't-Hooft anomalies. Such global anomalies can be canceled by anomalous symmetry-preserving topological quantum field theories (TQFTs), which contain no local point operators but only extended excitations such as line and surface"
485,,Mod $p$ Poincaré duality for $p$-adic period domains,Guillaume Pignon-Ywanne,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25029v1,https://arxiv.org/pdf/2512.25029v1,arxiv,,"In this article, we introduce a new class of smooth partially proper rigid analytic varieties over a $p$-adic field that satisfy Poincaré duality for étale cohomology with mod $p$-coefficients : the varieties satisfying ""primitive comparison with compact support"". We show that almost proper varietie"
486,,Real Riemann Surfaces: Smooth and Discrete,Johanna Düntsch; Felix Günther,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25022v1,https://arxiv.org/pdf/2512.25022v1,arxiv,,This paper develops a discrete theory of real Riemann surfaces based on quadrilateral cellular decompositions (quad-graphs) and a linear discretization of the Cauchy-Riemann equations. We construct a discrete analogue of an antiholomorphic involution and classify the topological types of discrete re
487,,Bounding regularity of $\mathrm{VI}^m$-modules,Wee Liang Gan; Khoa Ta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25010v1,https://arxiv.org/pdf/2512.25010v1,arxiv,,Fix a finite field $\mathbb{F}$. Let $\mathrm{VI}$ be a skeleton of the category of finite dimensional $\mathbb{F}$-vector spaces and injective $\mathbb{F}$-linear maps. We study $\mathrm{VI}^m$-modules over a noetherian commutative ring in the nondescribing characteristic case. We prove that if a f
488,,The splitting field and generators of the elliptic surface $Y^2=X^3 +t^{360} +1$,Sajad Salami,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25009v1,https://arxiv.org/pdf/2512.25009v1,arxiv,,"The splitting field of an elliptic surface $\mathcal{E}/\mathbb{Q}(t)$ is the smallest finite extension $\mathcal{K} \subset \mathbb{C}$ such that all $\mathbb{C}(t)$-rational points are defined over $\mathcal{K}(t)$. In this paper, we provide a symbolic algorithmic approach to determine the splitti"
489,,Fast Poisson brackets and constraint algebras in canonical gravity,Will Barker,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25007v1,https://arxiv.org/pdf/2512.25007v1,arxiv,,"In the study of alternative or extended theories of gravity, Dirac's Hamiltonian constraint algorithm is invaluable for enumerating the propagating modes and gauge symmetries. For gravity, this canonical approach is frequently applied as a means for finding pathologies such as strongly coupled modes"
490,,"Distributions of wide binary stars in theory and in Gaia data: III. Orbital momenta, masses, and manifestations of MOND",Valeri V. Makarov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25002v1,https://arxiv.org/pdf/2512.25002v1,arxiv,,"Using the censored catalog of 103,169 resolved Gaia DR3 binary stars with accurate astrometric data for each component, a new observable, object-specific parameter is computed for each pair: the projected orbital momentum. This parameter is the product of four functions of physical characteristics: "
491,,Dissipative corrections to the particle momentum spectrum of a decoupling fluid,Francesco Becattini; Daniele Roselli; Xin-Li Sheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24994v1,https://arxiv.org/pdf/2512.24994v1,arxiv,,"We present an \emph{ab initio} calculation within quantum statistical field theory and linear response theory, of the dissipative correction to the momentum spectrum of scalar particles emitted at decoupling (freeze-out) from a relativistic fluid assuming the initial state to be in local thermodynam"
492,,DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments,Yohan Park; Hyunwoo Ha; Wonjun Jo; Tae-Hyun Oh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24985v1,https://arxiv.org/pdf/2512.24985v1,arxiv,,"Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light cond"
493,,The Supersymmetry of Cuts in Pure Gauge Theory and Gravity,Jacob L. Bourjaily,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24984v1,https://arxiv.org/pdf/2512.24984v1,arxiv,,"At tree-level, scattering amplitudes involving only gluons or gravitons are unaffected by supersymmetry, allowing them to be efficiently encoded by and extracted from those of maximally supersymmetric (N=4,8) theories. This fails beyond tree-level, of course, but much less than would be expected. We"
494,,Optical Spiking Neural Networks via Rogue-Wave Statistics,Bahadır Utku Kesgin; Gülsüm Yaren Durdu; Uğur Teğin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24983v1,https://arxiv.org/pdf/2512.24983v1,arxiv,,"Optical computing could reduce the energy cost of artificial intelligence by leveraging the parallelism and propagation speed of light. However, implementing nonlinear activation, essential for machine learning, remains challenging in low-power optical systems dominated by linear wave physics. Here,"
495,,A Modal Logic for Possibilistic Reasoning with Fuzzy Formal Contexts,Prosenjit Howlader; Churn-Jung Liau,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24980v1,https://arxiv.org/pdf/2512.24980v1,arxiv,,We introduce a two-sort weighted modal logic for possibilistic reasoning with fuzzy formal contexts. The syntax of the logic includes two types of weighted modal operators corresponding to classical necessity ($\Box$) and sufficiency ($\boxminus$) modalities and its formulas are interpreted in fuzzy
496,,"Semiclassics, branes, and extremality",Adolfo Holguin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24979v1,https://arxiv.org/pdf/2512.24979v1,arxiv,,"We revisit the problem of computing extremal and non-extremal three point functions of semiclassical probes with single trace operators and point out certain inconsistencies in previous approaches in the literature. We clarify the roles of wavefunctions and averaging over moduli, concluding that hol"
497,,Attribution-Guided Distillation of Matryoshka Sparse Autoencoders,Cristina P. Martin-Linares; Jonathan P. Ling,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24975v1,https://arxiv.org/pdf/2512.24975v1,arxiv,,"Sparse autoencoders (SAEs) aim to disentangle model activations into monosemantic, human-interpretable features. In practice, learned features are often redundant and vary across training runs and sparsity levels, which makes interpretations difficult to transfer and reuse. We introduce Distilled Ma"
498,,Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions,Itallo Patrick Castro Alves Da Silva; Emanuel Adler Medeiros Pereira; Erick de Andrade Barboza; Baldoino Fonseca dos Santos Neto; Marcio de Medeiros Ribeiro,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24971v1,https://arxiv.org/pdf/2512.24971v1,arxiv,,"Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision syst"
499,,Large language models and the entropy of English,Colin Scheibner; Lindsay M. Smith; William Bialek,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24969v1,https://arxiv.org/pdf/2512.24969v1,arxiv,,"We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interact"
500,,The least prime with a given cycle type,Peter J. Cho; Robert J. Lemke Oliver; Asif Zaman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24963v1,https://arxiv.org/pdf/2512.24963v1,arxiv,,"Let $G$ be a finite group. Let $K/k$ be a Galois extension of number fields with Galois group isomorphic to $G$, and let $C \subseteq \mathrm{Gal}(K/k) \simeq G$ be a conjugacy invariant subset. It is well known that there exists an unramified prime ideal $\mathfrak{p}$ of $k$ with Frobenius element"
501,,Fundamental Limits for Near-Field Sensing -- Part I: Narrow-Band Systems,Tong Wei; Kumar Vijay Mishra; Bhavani Shankar M. R.; Björn Ottersten,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24958v1,https://arxiv.org/pdf/2512.24958v1,arxiv,,"Extremely large-scale antenna arrays (ELAAs) envisioned for 6G enable high-resolution sensing. However, the ELAAs worked in extremely high frequency will push operation into the near-field region, where spherical wavefronts invalidate classical far-field models and alter fundamental estimation limit"
502,,Simulations of two-dimensional single-mode Rayleigh-Taylor Instability using front-tracking/ghost-fluid method: comparison to experiments and theory,James Burton; Tulin Kaman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24949v1,https://arxiv.org/pdf/2512.24949v1,arxiv,,"Two-dimensional single-mode Rayleigh-Taylor Instability (RTI) is simulated using an accurate and robust front-tracking/ghost-fluid method (FT/GFM) with high-order weighted essentially non-oscillatory (WENO) scheme. We compare our numerical results with the single-mode RTI experiments of Renoult, Ros"
503,,RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment,Chenji Lu; Zhuo Chen; Hui Zhao; Zhenyi Wang; Pengjie Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24943v1,https://arxiv.org/pdf/2512.24943v1,arxiv,,"Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient complexity for comprehensive model assessment, resulting in an absence of standardized relevance evaluation metrics across"
504,,Iterative Deployment Improves Planning Skills in LLMs,Augusto B. Corrêa; Yoav Gelberg; Luckeciano C. Melo; Ilia Shumailov; André G. Pereira,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24940v1,https://arxiv.org/pdf/2512.24940v1,arxiv,,"We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change the properties of the resultant models. By testing this mechanism on various planning domains, we observe substantial i"
505,,"Vibe Coding, Interface Flattening",Hongrui Jin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24939v1,https://arxiv.org/pdf/2512.24939v1,arxiv,,"Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct m"
506,,Modelling the movements of organisms by stochastic theory in a comoving frame,Norberto Lucero Azuara; Rainer Klages,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24937v1,https://arxiv.org/pdf/2512.24937v1,arxiv,,Imagine you walk in a plane. You move by making a step of a certain length per time interval in a chosen direction. Repeating this process by randomly sampling step length and turning angle defines a two-dimensional random walk in what we call comoving frame coordinates. This is precisely how Ross a
507,,Green's function on the Tate curve,An Huang; Rebecca Rohrlich; Yaojia Sun; Eric Whyman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24935v1,https://arxiv.org/pdf/2512.24935v1,arxiv,,"Motivated by the question of defining a $p$-adic string worldsheet action in genus one, we define a Laplacian operator on the Tate curve, and study its Green's function. We show that the Green's function exists. We provide an explicit formula for the Green's function, which turns out to be a non-Arc"
508,,Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline,Minjun Zhao; Xinyu Zhang; Shuai Zhang; Deyang Li; Ruifeng Shi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24933v1,https://arxiv.org/pdf/2512.24933v1,arxiv,,"Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter"
509,,Generalised Hermite-Einstein Fibre Metrics and Slope Stability for Holomorphic Vector Bundles,Dan Popovici,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24932v1,https://arxiv.org/pdf/2512.24932v1,arxiv,,"Let $X$ be a compact complex manifold of dimension $n$ and let $m$ be a positive integer with $m\leq n$. Assume that $X$ admits a Kähler metric $ω$ and a weakly positive, $\partial\bar\partial$-closed, smooth $(n-m,\,n-m)$-form $Ω$. We introduce the notions of $(ω,\,Ω)$-Hermite-Einstein holomorphic "
510,,Introduction to black hole thermodynamics,Pietro Benetti Genolini,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24929v1,https://arxiv.org/pdf/2512.24929v1,arxiv,,"These are the lecture notes for a course at the ""Roberto Salmeron School in Mathematical Physics"" held at the University of Brasilia in September 2025, to be published in the proceedings book ""Modern topics in mathematical physics."" The course provides a concise and biased introduction to black hole"
511,,Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach,Yuchen Jiao; Na Li; Changxiao Cai; Gen Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24927v1,https://arxiv.org/pdf/2512.24927v1,arxiv,,"Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inherently slower and that increasing discretization order is the primary path to faster generation. This paper challenges t"
512,,Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection,Bartłomiej Olber; Jakub Winter; Paweł Wawrzyński; Andrii Gamalii; Daniel Górniak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24922v1,https://arxiv.org/pdf/2512.24922v1,arxiv,,"3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving benchmarks, they often struggle to generalize across different domains - for instance, a model trained in the U.S. may perf"
513,,Valence quark distribution of the pion inside a medium with finite baryon density: A Nambu--Jona-Lasinio model approach,Ashutosh Dwibedi; Satyajit Puhan; Sabyasachi Ghosh; Harleen Dahiya,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24921v1,https://arxiv.org/pdf/2512.24921v1,arxiv,,We calculate the in-medium valence quark distribution of the pion immersed in a finite baryon density using the light-cone quark model. The medium-modified pion properties are obtained by using the constituent quark mass-dependent light cone wave functions. To obtain the constituent quark masses at 
514,,Transgression in the primitive cohomology,Hao Zhuang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24920v1,https://arxiv.org/pdf/2512.24920v1,arxiv,,"We study the Chern-Weil theory for the primitive cohomology of a symplectic manifold. First, given a symplectic manifold, we review the superbundle-valued forms on this manifold and prove a primitive version of the Bianchi identity. Second, as the main result, we prove a transgression formula associ"
515,,Property (T) and Poincaré duality in dimension three,Cameron Gates Rudd,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24919v1,https://arxiv.org/pdf/2512.24919v1,arxiv,,"We use a recent result of Bader and Sauer on coboundary expansion to prove residually finite three-dimensional Poincaré duality groups never have property (T). This implies such groups are never Kähler. The argument applies to fundamental groups of (possibly non-aspherical) compact 3-manifolds, givi"
516,,Frequent subgraph-based persistent homology for graph classification,Xinyang Chen; Amaël Broustet; Guoting Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24917v1,https://arxiv.org/pdf/2512.24917v1,arxiv,,"Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. However, most PH methods on graphs rely on a limited set of filtrations, such as degre"
517,,AI-Driven Cloud Resource Optimization for Multi-Cluster Environments,Vinoth Punniyamoorthy; Akash Kumar Agarwal; Bikesh Kumar; Abhirup Mazumder; Kabilan Kannan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24914v1,https://arxiv.org/pdf/2512.24914v1,arxiv,,"Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dyna"
518,,On Diophantine exponents of lattices,Nikolay Moshchevitin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24913v1,https://arxiv.org/pdf/2512.24913v1,arxiv,,We describe the spectrum of ordinary Diophantine exponents for $d$-dimensional lattices. The result reduces the problem to two-dimensional case and uses argument of metric theory.
519,,Non-Equilibrium Dynamics in QCD and Holography,Matthias Kaminski,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24909v1,https://arxiv.org/pdf/2512.24909v1,arxiv,,"The plasma generated in heavy ion collisions goes through different phases in its time evolution. While early times right after the collision are governed by far-from equilibrium dynamics, later times are believed to be well described by near-equilibrium dynamics. While the regimes of non-equilibriu"
520,,Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes,Debasis Maji; Arghya Banerjee; Debaditya Barman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24901v1,https://arxiv.org/pdf/2512.24901v1,arxiv,,"Cognitive task classification using machine learning plays a central role in decoding brain states from neuroimaging data. By integrating machine learning with brain network analysis, complex connectivity patterns can be extracted from functional magnetic resonance imaging connectomes. This process "
521,,PRISM: A hierarchical multiscale approach for time series forecasting,Zihao Chen; Alexandre Andre; Wenrui Ma; Ian Knight; Sergey Shuvaev,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24898v1,https://arxiv.org/pdf/2512.24898v1,arxiv,,"Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we pr"
522,,Self-Supervised Amortized Neural Operators for Optimal Control: Scaling Laws and Applications,Wuzhe Xu; Jiequn Han; Rongjie Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24897v1,https://arxiv.org/pdf/2512.24897v1,arxiv,,"Optimal control provides a principled framework for transforming dynamical system models into intelligent decision-making, yet classical computational approaches are often too expensive for real-time deployment in dynamic or uncertain environments. In this work, we propose a method based on self-sup"
523,,Interior structure of black holes with nonlinear terms,Zi-Qiang Zhao; Zhang-Yu Nie; Xing-Kun Zhang; Yu-Sen An; Jing-Fei Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24893v1,https://arxiv.org/pdf/2512.24893v1,arxiv,,We investigate the oscillation of the Kasner exponent $p_t$ near critical point of the hairy black holes dual to holographic superfluid and reveal a clear inverse periodicity $f(T_c/(T_c-T))$ in a large region below the critical temperature. We first introduce the fourth-power term with a coefficien
524,,Bubbling wormholes and matrix models,Panos Betzios; Ji Hoon Lee; Olga Papadoulaki; Yanjun Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24891v1,https://arxiv.org/pdf/2512.24891v1,arxiv,,The thermofield double state entangles two copies of a CFT via a sum over energy eigenstates and is dual to the two-sided eternal black hole. We explore an analogous construction using sums over gauge group representations of half-BPS Wilson loops in multiple copies of $U(N)$ $\mathcal{N}=4$ super Y
525,,Coherent span-valued 2D TQFTs,Sophia E Marx; Rajan Amit Mehta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24887v1,https://arxiv.org/pdf/2512.24887v1,arxiv,,"We consider commutative Frobenius pseudomonoids in the bicategory of spans, and we show that they are in correspondence with 2-Segal cosymmetric sets. Such a structure can be interpreted as a coherent 2-dimensional topological quantum field theory taking values in the bicategory of spans. We also de"
526,,Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves,Tyler Hanks; Cristian F. Nino; Joana Bou Barcelo; Austin Copeland; Warren Dixon,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24886v1,https://arxiv.org/pdf/2512.24886v1,arxiv,,"Multi-agent target tracking in the presence of nonlinear dynamics and agent heterogeneity, where state-space dimensions may differ, is a challenging problem that traditional graph Laplacian methods cannot easily address. This work leverages the framework of cellular sheaves, a mathematical generaliz"
527,,BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts,Hengli Li; Zhaoxin Yu; Qi Shen; Chenxi Li; Mengmeng Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24885v1,https://arxiv.org/pdf/2512.24885v1,arxiv,,"Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversari"
528,,Probing quantum-coherent dynamics with free electrons,H. B. Crispin; N. Talebi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24883v1,https://arxiv.org/pdf/2512.24883v1,arxiv,,"Recent advances in time-resolved cathodoluminescence have enabled ultrafast studies of single emitters in quantum materials with femtosecond temporal resolution. Here, we develop a quantum theory modeling the dynamics of free electrons interacting with quantum emitters in arbitrary initial states. O"
529,,Description of Baryon Mass Spectrum by Open Strings and Diquarks,Yuki Fujimoto,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24882v1,https://arxiv.org/pdf/2512.24882v1,arxiv,,"We analyze the mass spectra of hadrons and demonstrate that the physical spectra of mesons and baryons are well described by the exponential spectrum of open strings. The open string spectrum, derived from string theory, is characterized by a unique Hagedorn temperature $T_{\rm H}$ and free from any"
530,,Exact Identity Linking Entropy Production and Mutual Information,Doohyeong Cho; Hawoong Jeong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24877v1,https://arxiv.org/pdf/2512.24877v1,arxiv,,Linking entropy production (EP) to information is a key step toward data-driven nonequilibrium thermodynamics. We derive an exact identity for overdamped Langevin dynamics that equates the total EP rate to the mutual-information rate between an infinitesimal displacement and its time-symmetric midpo
531,,Insights on the homogeneous $3$-local representations of the twin groups,Mohamad N. Nasser,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24874v1,https://arxiv.org/pdf/2512.24874v1,arxiv,,"We provide a complete classification of the homogeneous $3$-local representations of the twin group $T_n$, the virtual twin group $VT_n$, and the welded twin group $WT_n$, for all $n\geq 4$. Beyond this classification, we examine the main characteristics of these representations, particularly their "
532,,"Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem",Weixun Wang; XiaoXiao Xu; Wanhe An; Fangwen Dai; Wei Gao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24873v1,https://arxiv.org/pdf/2512.24873v1,arxiv,,"Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce"
533,,Configuration Spaces of Finite Representation Type Algebras,Nima Arkani-Hamed; Hadleigh Frost; Pierre-Guy Plamondon; Giulio Salvatori; Hugh Thomas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24870v1,https://arxiv.org/pdf/2512.24870v1,arxiv,,"To every finite-dimensional $\mathbb C$-algebra $Λ$ of finite representation type we associate an affine variety. These varieties are a large generalization of the varieties defined by ""$u$ variables"" satisfying ""$u$-equations"", first introduced in the context of open string theory and moduli space "
534,,Characterization of Transfer Using Multi-task Learning Curves,András Millinghoffer; Bence Bolgár; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24866v1,https://arxiv.org/pdf/2512.24866v1,arxiv,,"Transfer effects manifest themselves both during training using a fixed data set and in inductive inference using accumulating data. We hypothesize that perturbing the data set by including more samples, instead of perturbing the model by gradient updates, provides a complementary and more fundament"
535,,Approximate Computation via Le Cam Simulability,Deniz Akdemir,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24860v1,https://arxiv.org/pdf/2512.24860v1,arxiv,,"We propose a decision-theoretic framework for computational complexity, complementary to classical theory: moving from syntactic exactness (Turing / Shannon) to semantic simulability (Le Cam). While classical theory classifies problems by the cost of exact solution, modern computation often seeks on"
536,,On a conjecture of Almgren II: area-minimizing submanifolds with fractal singular sets on almost any manifold,Zhenhua Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24859v1,https://arxiv.org/pdf/2512.24859v1,arxiv,,"This paper is the second in a two-part solution to Almgren's conjecture on the existence of area-minimizing submanifolds with fractal singular sets. In part one, we construct area-minimizing submanifolds with fractal singular sets on certain special manifolds. Here we continue our work and show that"
537,,Measuring Mixed-State Topological Invariant in Open Photonic Quantum Walk,Qin-Qin Wang; Xiao-Ye Xu; Yong-Jian Han; Chuan-Feng Li; Guang-Can Guo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24857v1,https://arxiv.org/pdf/2512.24857v1,arxiv,,"Pure-state manifestations of geometric phase are well established and have found applications across essentially all branches of physics, yet their generalization to mixed-state regimes remains largely unexplored experimentally. The Uhlmann geometric phase offers a natural extension of pure-state pa"
538,,Advances in Agentic AI: Back to the Future,Sergio Alvarez-Telena; Marta Diez-Fernandez,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24856v1,https://arxiv.org/pdf/2512.24856v1,arxiv,,"In light of the recent convergence between Agentic AI and our field of Algorithmization, this paper seeks to restore conceptual clarity and provide a structured analytical framework for an increasingly fragmented discourse. First, (a) it examines the contemporary landscape and proposes precise defin"
539,,QCD Wehrl and entanglement entropies in a gluon spectator model at small-$x$,Gabriel Rabelo-Soares; Reinaldo Francener; Gabriel S. Ramos; Giorgio Torrieri,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24855v1,https://arxiv.org/pdf/2512.24855v1,arxiv,,"Recent studies have shown that hadronic multiplicity in deep inelastic scattering is associated with an entanglement entropy. However, such definitions are intrinsically longitudinal and do not capture the full phase--space structure of the proton. In this work, we investigate the Wehrl entropy of t"
540,,"Manifold-Constrained Sentence Embeddings via Triplet Loss: Projecting Semantics onto Spheres, Tori, and Möbius Strips",Vinit K. Chavan,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2505.00014,https://www.semanticscholar.org/paper/04bc6f7d6e660b62e7f55a309e24e7bd87e9eb88,,semantic_scholar,,"Recent advances in representation learning have emphasized the role of embedding geometry in capturing semantic structure. Traditional sentence embeddings typically reside in unconstrained Euclidean spaces, which may limit their ability to reflect complex relationships in language. In this work, we "
541,,Dynamical Geometric Theory of Principal Bundle Constrained Systems: Strong Transversality Conditions and Variational Framework for Gauge Field Coupling,Dongzhe Zheng,2025,,,,,,6,0.000,0.000,,https://www.semanticscholar.org/paper/07b3d3c138b8c41624a4cfcadfb103266dc8040b,,semantic_scholar,,"This paper introduces a geometric mechanics framework for constrained systems on principal bundles through \emph{compatible pairs} $(\mathcal{D}, \lambda)$, addressing fundamental challenges in gauge-constrained physical systems. We characterize the strong transversality condition by pairing constra"
542,,Computation of connection-based Zagreb indices in chain graphs and triangular sheets,Muhammad Mudassar Hassan; A. Waqar; Haidar Ali; Parvez Ali,2024,Journal of coordination chemistry,,,,,5,0.000,0.000,10.1080/00958972.2024.2305819,https://www.semanticscholar.org/paper/c4678c934de620dc5321105ec4a88f8207dd6604,,semantic_scholar,,"Abstract Graph theory is a mathematical framework that can be used to model and analyze complex networks. Topology plays a key role in determining the compatibility of chemical graphs. Topological indices have been widely applied in a wide range of domains, such as chemistry, biological activity pre"
543,,Numerical Simulations and Bifurcation of Ca2+ Oscillatory Behaviour in the Connection of Neurons and Astrocytes,Hemlata Jethanandani̇; B. Jha,2024,Cell Biochemistry and Biophysics,,,,,0,0.000,0.000,10.1007/s12013-024-01427-1,https://www.semanticscholar.org/paper/9ff019beb4e7a5e1d7d871088d3ad0d18265eacb,,semantic_scholar,,
544,,Constrained Branching Search for Topology Identification Stream Computing With Lightweight Implementation,Zhuoheng Wang; Jie Gao; Qiushi Cui; Yang Weng,2025,IEEE Transactions on Power Systems,,,,,1,0.000,0.000,10.1109/TPWRS.2024.3510940,https://www.semanticscholar.org/paper/91b6244b4f5ed43ea339bacdf1b4eac2ac3acf87,,semantic_scholar,,"Accurate topological awareness is critical to the stability of low-voltage distribution networks (LVDNs). However, traditional impedance-based topology restoration assumes accuracy that is often unattainable due to impedance data inaccuracy. Given LVDN sensor quality, robustness against data quality"
545,,A Differential Manifold Perspective and Universality Analysis of Continuous Attractors in Artificial Neural Networks,Shaoxin Tian; Hongkai Liu; Yuying Yang; Jiali Yu; Zizheng Miao,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2509.10514,https://www.semanticscholar.org/paper/442477ffcc9360823bd8a0b47c9b13c30ad19da1,,semantic_scholar,,"Continuous attractors are critical for information processing in both biological and artificial neural systems, with implications for spatial navigation, memory, and deep learning optimization. However, existing research lacks a unified framework to analyze their properties across diverse dynamical "
546,,Hyper-differential sensitivity analysis for inverse problems constrained by partial differential equations,Isaac Sunseri; Joseph L. Hart; Bart van Bloemen Waanders; A. Alexanderian,2020,Inverse Problems,,,,,23,0.000,0.000,10.1088/1361-6420/abaf63,https://www.semanticscholar.org/paper/6dee5be22510e13a7d228b3ebb9cb85f650df7f7,https://arxiv.org/pdf/2003.00978,semantic_scholar,,"High fidelity models used in many science and engineering applications couple multiple physical states and parameters. Inverse problems arise when a model parameter cannot be determined directly, but rather is estimated using (typically sparse and noisy) measurements of the states. The data is usual"
547,,Advancing Mathematical Research via Human-AI Interactive Theorem Proving,Chenyi Li; Zhijian Lai; Dong An; Jiang Hu; Zaiwen Wen,2025,,,,,,2,0.000,0.000,,https://www.semanticscholar.org/paper/3fdfca6bfbb95d382ac285fdef221592c9f8ee83,,semantic_scholar,,We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible as
548,,Global thermodynamic manifold for conservative control of stochastic systems,Jordan R. Sawchuk; David A. Sivak,2024,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/430a0e42e4a0350783cdac1c76b7ec4682914610,,semantic_scholar,,"Optimal control of stochastic systems plays a central role in nonequilibrium physics, with applications in the study of biological molecular motors and the design of single-molecule experiments. While exact analytic solutions to optimization problems are rare, under slow driving conditions, the prob"
549,,DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization,Gang Li; Ming Lin; Tomer Galanti; Zhengzhong Tu; Tianbao Yang,2025,arXiv.org,,,,,7,0.000,0.000,10.48550/arXiv.2505.12366,https://www.semanticscholar.org/paper/2ab138c8ad6be7bfa4c001fdc51232e687c878be,,semantic_scholar,,"The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning models (LRMs). In this work, we analyze the GRPO objective under a binary reward setting and reveal an inherent limitat"
550,,Transformers from Diffusion: A Unified Framework for Neural Message Passing,Qitian Wu; David Wipf; Junchi Yan,2024,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/d545bf74d07796e614eb41c540a5bf4a8d151e62,,semantic_scholar,,"Learning representations for structured data with certain geometries (e.g., observed or unobserved) is a fundamental challenge, wherein message passing neural networks (MPNNs) have become a de facto class of model solutions. In this paper, inspired by physical systems, we propose an energy-constrain"
551,,Digital Construction Planning for Resource-Constrained Projects,Yan Lin,2025,Proceedings of the 2025 8th International Conference on Computer Information Science and Artificial Intelligence,,,,,0,0.000,0.000,10.1145/3773365.3773545,https://www.semanticscholar.org/paper/fed40af67db73ee4a21c66a79b182b9695494b9a,,semantic_scholar,,"Aiming at the problem of construction period delay caused by unreasonable initial construction period design and schedule arrangement in the construction process of engineering projects, this paper explores a digital construction planning method for resource-constrained projects based on the trade-o"
552,,Graph-MARL: A Neuro-Symbolic Autonomy Framework for Multi-Chaser Active Debris Removal Task Allocation and Path Planning,Dandan Su; K. A. Neusypin; Ge Dong,2025,2025 IEEE International Conference on Unmanned Systems (ICUS),,,,,0,0.000,0.000,10.1109/ICUS66297.2025.11294460,https://www.semanticscholar.org/paper/8b82ea957d7ce5afca46743618ebfda5d5dc0567,,semantic_scholar,,"The escalating density of objects in Low Earth Orbit (LEO) renders Active Debris Removal (ADR) critical for ensuring the safety of space assets and the sustainability of the orbital environment. However, planning missions for a multi-chaser spacecraft constellation presents a complex, Astro dynamica"
553,,The Connection on Fiber Bundles,Abd Rahman,2022,INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH,,,,,0,0.000,0.000,10.47191/ijmcr/v10i7.04,https://www.semanticscholar.org/paper/824c048a2cabf14d9959d608f3b23893148b97d7,https://ijmcr.in/index.php/ijmcr/article/download/438/367,semantic_scholar,,"A connection is a device that defines the concept of parallel transport on a bundle, that is identifies fibers over nearby points. Fiber bundles form are the natural mathematical framework for the gauge filed theories. Also affine connection is the most elementary type of connection, a means of para"
554,,Modeling Molecular Interactions with Hyper-Networks and Super-Hyper-networks,Takaaki Fujita; Muhammad Gulistan; Arkan A. Ghaib,2025,Advances in Research,,,,,0,0.000,0.000,10.9734/air/2025/v26i41412,https://www.semanticscholar.org/paper/7658e365a780ca89498c140a52ca4af3ee90dcac,,semantic_scholar,,"Graph theory examines the structure of networks by treating entities as vertices and the connections between them as edges. A Hyper-Graph enhances this framework by permitting a single Hyper-edge to link multiple vertices at once. Building on that idea, a Super-Hyper-Graph introduces layers of recur"
555,,Dynamic AP-UE Association and Power Allocation in Sparse LSFD for Energy-Constrained Networks,Shaik Karimullah; S. Fahimuddin; T. N. Ranganadham; P. Hariobulesu; R.Soma Sekhar,2025,"2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE)",,,,,0,0.000,0.000,10.1109/ICCECE61355.2025.10941057,https://www.semanticscholar.org/paper/41096550bcaba7f47e0521077d153f955112600f,,semantic_scholar,,"The rise of 5G and beyond has intensified the need for energy-efficient, high-capacity wireless networks, especially in IoT, smart cities, and dense urban settings. Sparse Large-Scale Fading Decoding (S-LSFD) addresses this challenge by dynamically associating User Equipment (UE) with a selective su"
556,,Physics-constrained normalizing flow for identification and modeling of vortex-induced vibration in stay cables,Zhe Wang; Zhiping Mao; Shanwu Li; Yongchao Yang,2025,Nonlinear dynamics,,,,,0,0.000,0.000,10.1007/s11071-025-11808-7,https://www.semanticscholar.org/paper/6deff5669b35c03cc76d9aca92d728d767457d59,,semantic_scholar,,
557,,Stochastic Mechanics: The Unification of Quantum Mechanics with Brownian Motion,F. Kuipers,2023,SpringerBriefs in Physics,,,,,19,0.000,0.000,10.1007/978-3-031-31448-3,https://www.semanticscholar.org/paper/2c992e3b47c4b77f633cb50ad938dd6a4612f75b,,semantic_scholar,,"We unify Brownian motion and quantum mechanics in a single mathematical framework. In particular, we show that non-relativistic quantum mechanics of a single spinless particle on a flat space can be described by a Wiener process that is rotated in the complex plane. We then extend this theory to rel"
558,,Geo-PhysNet: A Geometry-Aware and Physics-Constrained Graph Neural Network for Aerodynamic Pressure Prediction on Vehicle Fluid–Solid Surfaces,Bowen Liu; Hao Wang; Liheng Xue; Yin Long,2025,Applied Sciences,,,,,0,0.000,0.000,10.3390/app152111645,https://www.semanticscholar.org/paper/39d2ee645bea2279e81f9c393aa9f92513578f97,,semantic_scholar,,"The aerodynamic pressure of a car is crucial for its shape design. To overcome the time-consuming and costly bottleneck of wind tunnel tests and computational fluid dynamics (CFD) simulations, deep learning-based surrogate models have emerged as highly promising alternatives. However, existing metho"
559,,Local Thermal Operations and Classical Communication,Rafal Bistro'n; Jakub Czartowski,2024,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/9e296f97e03339c04b9ef8817f8c85d9866a09ae,,semantic_scholar,,"In quantum thermodynamics, understanding the interplay between locality, thermal constraints, and communication remains an open challenge. In this manuscript, we introduce Local Thermal Operations and Classical Communication (LTOCC), a novel operational framework that unifies the distant laboratorie"
560,,A Formal Approach to Optimally Configure a Fully Connected Multilayer Hybrid Neural Network,Goutam Chakraborty; V. Azhmyakov; Luz Adriana Guzman Trujillo,2024,Mathematics,,,,,1,0.000,0.000,10.3390/math13010129,https://www.semanticscholar.org/paper/b3db2c99afe5331efd518378e6b8b52bdfca63d9,,semantic_scholar,,"This paper is devoted to a novel formal analysis, optimizing the learning models for feedforward multilayer neural networks with hybrid structures. The proposed mathematical description replicates a specific switched-type optimal control problem (OCP). We have developed an equivalent, optimal contro"
561,,Nonlinear Dynamical Model and Analysis of Emotional Propagation Based on Caputo Derivative,Liang Hong; Lipu Zhang,2025,Mathematics,,,,,0,0.000,0.000,10.3390/math13132044,https://www.semanticscholar.org/paper/d36d8538a30af37158f785eeb34e739f31b62f96,,semantic_scholar,,"Conventional integer-order models fail to adequately capture non-local memory effects and constrained nonlinear interactions in emotional dynamics. To address these limitations, we propose a coupled framework that integrates Caputo fractional derivatives with hyperbolic tangent–based interaction fun"
562,,"Constrained Dynamics, Stochastic Numerical Methods and the Modeling of Complex Systems",B. Leimkuhler; Richard Tsai; Gilles Vilmart; Rachel Ward,2024,Oberwolfach Reports,,,,,1,0.000,0.000,10.4171/owr/2024/26,https://www.semanticscholar.org/paper/84dad5d0ba35822145bb9df6cd454b3f4ef35726,https://ems.press/content/serial-article-files/49484,semantic_scholar,,"The workshop aimed to unite researchers from diverse fields of mathematics and statistics to explore the foundations of high-dimensional modeling and computational studies. It addressed recent advancements in numerical analysis, dynamical systems, and stochastic differential equations that support m"
563,,Hyper-differential sensitivity analysis with respect to model discrepancy: mathematics and computation,Joseph L. Hart; B. V. B. Waanders,2022,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2210.09037,https://www.semanticscholar.org/paper/9b3975ee3ddcda50fca0321ed7889ca3f6fb97bf,https://arxiv.org/pdf/2210.09037,semantic_scholar,,"Model discrepancy, defined as the difference between model predictions and reality, is ubiquitous in computational models for physical systems. It is common to derive partial differential equations (PDEs) from first principles physics, but make simplifying assumptions to produce tractable expression"
564,,"On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatment",S. Damelin,2021,,,,,,2,0.000,0.000,,https://www.semanticscholar.org/paper/00fa67298edfb51d69a830f539b3085351c545cf,,semantic_scholar,,"This paper provides fascinating connections between several mathematical problems which lie on the intersection of several mathematics subjects, namely algebraic geometry, approximation theory, complex-harmonic analysis and high dimensional data science. Modern techniques in algebraic geometry, appr"
565,,Hyperspectral subpixel unmixing via an integrative framework,Chunzhi Li; Xiaohua Chen; Yuan Zhang,2020,,,,,,1,0.000,0.000,10.1080/01431161.2020.1783711,https://www.semanticscholar.org/paper/c947cdc476ae597671bb4d4f923118b60eadfefb,,semantic_scholar,,"ABSTRACT In hyperspectral applications, spectral unmixing (SU) is an important technology to obtain the endmembers and the fractional land covers. Spectral variability, outliers, and nonlinearity are three challenging issues, causing SU to extract endmembers and corresponding abundance maps inaccura"
566,,From Barthel–Randers–Kropina Geometries to the Accelerating Universe: A Brief Review of Recent Advances in Finslerian Cosmology,A. Bouali; H. Chaudhary; L. Csillag; Rattanasak Hama; T. Harko,2025,Universe,,,,,3,0.000,0.000,10.3390/universe11070198,https://www.semanticscholar.org/paper/eefe07bdf746f006400e52eccdc94d868a732831,,semantic_scholar,,"We present a review of recent developments in cosmological models based on Finsler geometry, as well as geometric extensions of general relativity formulated within this framework. Finsler geometry generalizes Riemannian geometry by allowing the metric tensor to depend not only on position but also "
567,,Incompatible Deformations in Relativistic Elasticity,S. Lychev; K. Koifman; N. A. Pivovaroff,2023,Lobachevskii Journal of Mathematics,,,,,2,0.000,0.000,10.1134/S1995080223060343,https://www.semanticscholar.org/paper/efb7876fd828ab8f3c15e2c39fa127cc5688b18b,,semantic_scholar,,
568,,A Theory of Functional Connections-Based hp-Adaptive Mesh Refinement Algorithm for Solving Hypersensitive Two-Point Boundary-Value Problems,K. Drozd; Roberto Furfaro; Andrea D’Ambrosio,2024,Mathematics,,,,,1,0.000,0.000,10.3390/math12091360,https://www.semanticscholar.org/paper/cdd809a9e6328fced9561e0385680b88cb80a231,https://www.mdpi.com/2227-7390/12/9/1360/pdf?version=1714406956,semantic_scholar,,This manuscript introduces the first hp-adaptive mesh refinement algorithm for the Theory of Functional Connections (TFC) to solve hypersensitive two-point boundary-value problems (TPBVPs). The TFC is a mathematical framework that analytically satisfies linear constraints using an approximation meth
569,,Plato’s Allegory of the ‘Cave’ and Hyperspaces: Sonic Representation of the ‘Cave’ as a Four-Dimensional Acoustic Space via an Interactive Art Application,Dimitrios Traperas; Andreas Floros; N. Kanellopoulos,2024,AppliedMath,,,,,0,0.000,0.000,10.3390/appliedmath4030052,https://www.semanticscholar.org/paper/58d9c906630243e25ed34ef3ce54f6796bbc4a4f,https://www.mdpi.com/2673-9909/4/3/52/pdf?version=1724299490,semantic_scholar,,"Mathematician and philosopher Charles Howard Hinton posited a plausible correlation between higher-dimensional spaces, also referred to as ‘hyperspaces’, and the allegorical concept articulated by the Ancient Greek philosopher Plato in his work, Republic, known as the ‘Cave.’ In Plato’s allegory, in"
570,,Higgs Branches in the Omega-background via the Category of Line Operators,Thomas Karabela; Wenjun Niu,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/e1cf61cde59a817e46664a18d544015485fdb82c,,semantic_scholar,,"The vacuum manifold $\mathcal{M}$ of a topological twist of a 3d $\mathcal{N}=4$ gauge theory is a hyper-K\""ahler variety; deformations and quantizations of $\mathcal{M}$ can be constructed in the framework of 3 dimensional topological quantum field theories. In particular, based on physics argument"
571,,Topology and knot theory applications in quantum field theory,Chaitali Deshpande; Chandrashekhar Ramtirthkar; T. A. Wani; Varsha Kiran Bhosale; Monali Gulhane,2025,Journal of Interdisciplinary Mathematics,,,,,0,0.000,0.000,10.47974/jim-2370,https://www.semanticscholar.org/paper/d1bbb01e1a465fbc460fae7f518d4b9663e2726c,,semantic_scholar,,Topology and knot theory have profoundly influenced modern quantum field theory by revealing deep connections between gauge fields and topological invariants. Topological quantum field theories (TQFTs) disregard the spacetime metric and compute quantities that depend only on the global topology of t
572,,Spectral Theory and Hardy Spaces for Bessel Operators in Non-Standard Geometries,Saeed Hashemi Sababe,2025,Mathematics,,,,,3,0.000,0.000,10.3390/math13040565,https://www.semanticscholar.org/paper/0ae18009a2423ceb87eecc8f9ac06a55b7c51d49,https://doi.org/10.3390/math13040565,semantic_scholar,,"This paper develops novel results in the harmonic analysis of Bessel operators, extending their theory to higher-dimensional and non-Euclidean spaces. We present a refined framework for Hardy spaces associated with Bessel operators, emphasizing atomic decompositions, dual spaces, and connections to "
573,,A non-Newtonian approach to electromagnetic curves in optical fiber,Aykut Has; B. Yılmaz,2025,Revista mexicana de física,,,,,0,0.000,0.000,10.31349/revmexfis.71.051306,https://www.semanticscholar.org/paper/307e77baf0694e4e9a54e4c7b0f1d734c83099de,,semantic_scholar,,"The investigation within this article delves into the non-Newtonian geometric attributes exhibited by a linearly polarized light wave along an optical fiber within the framework of the 3D multiplicative Riemann manifold, employing multiplicative derivative and integral. While conducting this researc"
574,,Segmentation of the spacecraft transfer problem through overdetermined and continuity constraints based on the Theory of Functional Connections,Allan Kardec de Almeida Junior,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/947b30c6b9bf943b82a0cbc6a73bb97fdcfd2fe8,,semantic_scholar,,This paper introduces a segmented approach for solving constrained orbit transfer problems. The segments are connected through continuity constraints under the Theory of Functional Connections (TFC) mathematical framework that performs linear functional interpolation. This approach is further enhanc
575,,High-order expansion of Neural Ordinary Differential Equations flows,Dario Izzo; Sebastien Origer; Giacomo Acciarini; F. Biscani,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2504.08769,https://www.semanticscholar.org/paper/46e60e8ea9946c5aaacbe678b33b545b3229ecea,,semantic_scholar,,"Artificial neural networks, widely recognised for their role in machine learning, are now transforming the study of ordinary differential equations (ODEs), bridging data-driven modelling with classical dynamical systems and enabling the development of infinitely deep neural models. However, the prac"
576,,"Dual Affine Connections, Legendre Transforms, and Black Hole Thermodynamics",Shoshauna Gauvin,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/9a634935dfd147476f1fda8ddc84afdc5c276ac6,,semantic_scholar,,"Geometrical methods have become increasingly important in understanding both thermodynamics and information theory. In particular, dual affine (Hessian) geometry offers a powerful unification of concepts by recasting Legendre transformations as coordinate changes on a manifold endowed with a strictl"
577,,Solving the Problem of Poor Internet Connectivity in Dhaka: Innovative Solutions Using Advanced WebRTC and Adaptive Streaming Technologies,Pavel Malinovskiy,2025,International Research Journal of Modernization in Engineering Technology and Science,,,,,2,0.000,0.000,10.56726/IRJMETS68451,https://www.semanticscholar.org/paper/51bfae27a69adda7e0659a96af622eb029b496e4,,semantic_scholar,,"Dhaka, Bangladesh, one of the world's most densely populated cities, faces severe challenges in maintaining reliable, high-speed internet connectivity. This paper presents an innovative framework that addresses poor mobile data connections through the integration of advanced WebRTC technology with a"
578,,Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization,Jake B. Perazzone; Shiqiang Wang; Mingyue Ji; Kevin S. Chan,2025,IEEE Transactions on Networking,,,,,0,0.000,0.000,10.1109/TON.2025.3539857,https://www.semanticscholar.org/paper/f85432c40947f00f91765424fdad77c79a437b81,,semantic_scholar,,"Federated learning (FL) is a useful tool that enables the training of machine learning models over distributed data without having to collect data centrally. When deploying FL in constrained wireless environments, however, intermittent connectivity of devices, heterogeneous connection quality, and n"
579,,"Extension Research of Principal Bundle Constraint System Theory on Ricci-flat K\""ahler Manifolds",Dongzhe Zheng,2025,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/dd5ce52c0b3ac696e64f2c2e86910a715ca3c97a,,semantic_scholar,,This paper extends the geometric mechanics theory of constraint systems on principal bundles from the flat connection case to the general situation with non-zero curvature. Based on the theoretical foundation of compatible pairs under strong transversality conditions and principal bundle constraint 
580,,ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs,Bingxin Zhou; Outongyi Lv; Jing Wang; Xiang Xiao; Weishu Zhao,2025,Trans. Mach. Learn. Res.,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/3eef868a35888f318ca84f9810fa639ab324a63b,,semantic_scholar,,
581,,The Study on Modified Theories of General Relativity: A Differential Geometric Approach,N. S. Kavya,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/05bab74866e77d18734622c03ac29eec4825f47e,,semantic_scholar,,"The introduction of General Relativity (GR) in 1915 revolutionized our understanding of gravity, but over time, its limitations in explaining phenomena like dark energy, dark matter, and quantum gravity have motivated alternative theories. Early modifications, such as Weyl's 1919 proposal, focused o"
582,,From exchangeability to rational belief: a cognitive interpretation of de Finetti’s theorem,Tommaso Costa,2025,Frontiers in Psychology,,,,,0,0.000,0.000,10.3389/fpsyg.2025.1621552,https://www.semanticscholar.org/paper/22d003260508e4243e89e0b258ebeca6b8639f52,,semantic_scholar,,"Probabilistic reasoning is central to many theories of human cognition, yet its foundations are often presented through abstract mathematical formalisms disconnected from the logic of belief and learning. In this article, we propose a reinterpretation of de Finetti’s representation theorem as a prin"
583,,Measurability and continuity of parametric low-rank approximation in Hilbert spaces: linear operators and random variables,Nicola Rares Franco,2024,Revista Matemática Complutense,,,,,0,0.000,0.000,10.48550/arXiv.2409.09102,https://www.semanticscholar.org/paper/ac96b268db28eb03c5a03ec2719d6eb89992a305,,semantic_scholar,,"
 We present a unified theoretical framework for parametric low-rank approximation, a research area devoted to the development of efficient algorithms that act as adaptive alternatives of traditional methods such as Singular Value Decomposition (SVD), Proper Orthogonal Decomposition (POD), and Princ"
584,,Algorithmic and Machine Learning Methods for Witten Genus Vanishing Theorems,Song Xu,2025,2025 7th International Conference on Software Engineering and Computer Science (CSECS),,,,,0,0.000,0.000,10.1109/CSECS64665.2025.11009407,https://www.semanticscholar.org/paper/d8c33cf02956e22c9154b54265b2c522dfb97ba8,,semantic_scholar,,"Witten genus, a fundamental invariant in index theory, extends $A$-genus and plays a crucial role in differential topology and mathematical physics. A central problem concerns identifying conditions under which the Witten genus vanishes, with the Stolz conjecture proposing a connection to positive R"
585,,Random Relay Jamming in Cooperative Free Space Optical Systems,Pratiti Paul; M. Bhatnagar,2022,IEEE Systems Journal,,,,,11,0.000,0.000,10.1109/jsyst.2021.3099019,https://www.semanticscholar.org/paper/b4ca1b09d9fbcafd21830e5024a45ace376139cb,,semantic_scholar,,"Relay-assisted free space optical (FSO) systems offer several advantages such as cooperative diversity, mitigating turbulence-induced fading, and broad coverage area. Despite these manifold advantages, they can still be affected by unfavorable activities of jammer. Therefore, it is of high importanc"
586,,Orbit transfer using Theory of Functional Connections via change of variables,Allan K. de Almeida; Antonio F. B. A. Prado; D. Mortari,2023,The European Physical Journal Special Topics,,,,,8,0.000,0.000,10.1140/epjs/s11734-023-01013-1,https://www.semanticscholar.org/paper/73b9ac3572d2ea7434106e375817c532bfce2c96,,semantic_scholar,,"This work shows that a class of astrodynamics problems subject to mission constraints can be efficiently solved using the Theory of Functional Connections (TFC) mathematical framework by a specific change of coordinates. In these problems, the constraints are initially written in nonlinear and coupl"
587,,Traveling waves in a model for cortical spreading depolarization with slow-fast dynamics.,David Reyner-Parra; C. Bonet; T. M. Seara; G. Huguet,2023,Chaos,,,,,4,0.000,0.000,10.1063/5.0160509,https://www.semanticscholar.org/paper/59c57d2a615005a99b1bae6d4d52b55f0c842488,https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0160509/18626232/083154_1_5.0160509.pdf,semantic_scholar,,"Cortical spreading depression and spreading depolarization (CSD) are waves of neuronal depolarization that spread across the cortex, leading to a temporary saturation of brain activity. They are associated with various brain disorders such as migraine and ischemia. We consider a reduced version of a"
588,,End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing,G. Arya; William F. Li; C. Roques-Carmes; M. Soljačić; Steven G. Johnson,2022,ACS Photonics,,,,,27,0.000,0.000,10.1021/acsphotonics.4c00259,https://www.semanticscholar.org/paper/8b5a821e2c87653e0ec4df88548b6c75ab5f21bc,https://arxiv.org/pdf/2201.12348,semantic_scholar,,"We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero v"
589,,Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence,M. A. Yamin; P. Valsasina; J. Tessadori; M. Filippi; V. Murino,2023,Human Brain Mapping,,,,,6,0.000,0.000,10.1002/hbm.26210,https://www.semanticscholar.org/paper/e496c9dfa716f014507ed8e03e801346104ff2b6,https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26210,semantic_scholar,,Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting‐state (RS) functional connectivity (FC) abnormalities found in thi
590,,Hybrid geometrodynamics: a Hamiltonian description of classical gravity coupled to quantum matter,Jose Luis Alonso Buj; Carlos Bouthelier Madre; J. Clemente-Gallardo; David Martínez-Crespo,2023,Classical and quantum gravity,,,,,7,0.000,0.000,10.1088/1361-6382/ad3459,https://www.semanticscholar.org/paper/fc9d2bd9b8e61a00a64c67fa241d2fb17e5f889d,https://arxiv.org/pdf/2307.00922,semantic_scholar,,"We generalize the Hamiltonian picture of general relativity coupled to classical matter, known as geometrodynamics, to the case where such matter is described by a quantum field theory in curved spacetime, but gravity is still described by a classical metric tensor field over a spatial hypersurface "
591,,Topological indices of general relativity and Yang-Mills theory in four-dimensional space-time,Y. Kurihara,2022,,,,,,2,0.000,0.000,,https://www.semanticscholar.org/paper/780aaf3ccf064741913efeaf7f41243cb6ca9c39,,semantic_scholar,,"This report investigates general relativity and the Yang-Mills theory in four-dimensional space-time using a common mathematical framework, the Chern-Weil theory for principal bundles. The whole theory is described owing to the fibre bundle with the GL(4) symmetry by twisting several principal bundl"
592,,Stepsize anything: A unified learning rate schedule for budgeted-iteration training,Anda Tang; Yiming Dong; Yutao Zeng; zhou Xun; Zhouchen Lin,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2505.24452,https://www.semanticscholar.org/paper/5252fc9c44d42587da74fc166e18038fe8ee0263,,semantic_scholar,,"The expanding computational costs and limited resources underscore the critical need for budgeted-iteration training, which aims to achieve optimal learning within predetermined iteration budgets. While learning rate schedules fundamentally govern the performance of different networks and tasks, par"
593,,DigiLoCS: A leap forward in predictive organ-on-chip simulations,M. R. Aravindakshan; C. Mandal; A. Pothen; C. Maass,2024,bioRxiv,,,,,8,0.000,0.000,10.1101/2024.03.28.587123,https://www.semanticscholar.org/paper/8a848c4ba71a6947b76050835050141c7827e4ff,https://www.biorxiv.org/content/biorxiv/early/2024/03/29/2024.03.28.587123.full.pdf,semantic_scholar,,"Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach "
594,,OCTANE - Optimal Control for Tensor-based Autoencoder Network Emergence: Explicit Case,R. Khatri; Anthony Kolshorn; Colin Olson; Harbir Antil,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2509.08169,https://www.semanticscholar.org/paper/612607d5003ccec7cda114f93454e89cea1585d1,,semantic_scholar,,"This paper presents a novel, mathematically rigorous framework for autoencoder-type deep neural networks that combines optimal control theory and low-rank tensor methods to yield memory-efficient training and automated architecture discovery. The learning task is formulated as an optimization proble"
595,,Geometrical Modelling and Numerical Analysis of Dislocaion Mechanics,Shunsuke Kobayashi; R. Tarumi,2022,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2205.02443,https://www.semanticscholar.org/paper/06d4416902fe1137bd5c93b12ea53c0329088197,http://arxiv.org/pdf/2205.02443,semantic_scholar,,"This study undertakes the mathematical modelling and numerical analysis of dislocations within the framework of differential geometry. The fundamental configurations, i.e. reference, intermediate and current configurations, are expressed as the Riemann-Cartan manifold, which equips the Riemannian me"
596,,Quantifying the Structure of Disordered Materials,T. Hardin; M. Chandross; Rahul Meena; Spencer Fajardo; Dimitris G. Giovanis,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/10cb93831ff385c2d101ee4ad4390f58fe1f551c,,semantic_scholar,,"Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the latt"
597,,"Rate-induced tipping: thresholds, edge states and connecting orbits",Sebastian Wieczorek; Chunping Xie; P. Ashwin,2021,Nonlinearity,,,,,47,0.000,0.000,10.1088/1361-6544/accb37,https://www.semanticscholar.org/paper/c5c5e699b11a269df38f1b1ea5822a9903f385ca,https://iopscience.iop.org/article/10.1088/1361-6544/accb37/pdf,semantic_scholar,,"Rate-induced tipping (R-tipping) occurs when time-variation of input parameters of a dynamical system interacts with system timescales to give genuine nonautonomous instabilities. Such instabilities appear as the input varies at some critical rates and cannot, in general, be understood in terms of a"
598,,A Battery-connected Switched-Capacitor-based Power Step-Up Converter for V2G Applications,Hakan Tekin; Göknur Setrekli; Eren Murtulu; Hikmet Karşıyaka; Davut Ertekin,2023,International Conference on Electrical and Electronics Engineering,,,,,0,0.000,0.000,10.1109/ELECO60389.2023.10416060,https://www.semanticscholar.org/paper/30eac2064d9a636922707f64fc677d51e09e7962,,semantic_scholar,,"In this study, a novel power boost converter topology, which is characterized by a single-switch configuration and its connection to a battery, is introduced. The primary objective of this study is to explore its suitability for voltage enhancement in electrification transportation systems. An intri"
599,,Hojman-type conserved quantities for time-scale nonshifted mechanical systems,Shuang Hou; Chuanjing Song,2025,Physica Scripta,,,,,0,0.000,0.000,10.1088/1402-4896/ae2cff,https://www.semanticscholar.org/paper/ccf2247db3ac168ea3f4762bf99abd4740e0d99e,,semantic_scholar,,"Conserved quantity is one of the core topics in analytical mechanics. Identifying such quantities in a system not only reduces the number of degrees of freedom, thereby simplifying the dynamical description, but also reveals the invariant laws that the system follows in the complex evolution process"
600,,Robot-assisted mapping of chemical reaction hyperspaces and networks,Yankai Jia; Rafał Frydrych; Yaroslav I. Sobolev; Wai-Shing Wong; Bibek Prajapati,2025,Nature,,,,,3,0.000,0.000,10.1038/s41586-025-09490-1,https://www.semanticscholar.org/paper/db54e93e27f6702c48cb182bf529da4a4c97836b,,semantic_scholar,,"Despite decades of investigation, it remains unclear (and hard to predict1, 2, 3–4) how the outcomes of chemical reactions change over multidimensional ‘hyperspaces’ defined by reaction conditions5. Whereas human chemists can explore only a limited subset of these manifolds, automated platforms6, 7,"
601,,THE ABSTRACTS OF THE TALKS 2022 ALGEBRA AND BEYOND A CONFERENCE IN HONOR OF THE MATHEMATICAL CONTRIBUTIONS OF MICHAEL J. LARSEN,Chun Yin Hui; C. Simpson; A. Lindenstrauss; M. Wood; Shekhar Khare,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/57338a5c8efc977ae75eda4fd1421c2c745aa758,,semantic_scholar,,
602,,Preparing Code States via Seed-Entangler-Enriched Sequential Quantum Circuits: Application to Tetra-Digit Topological Error-Correcting Codes,Yu-Tao Hu; Meng-Yuan Li; Peng Ye,2025,,,,,,3,0.000,0.000,10.1103/d8gs-fnwt,https://www.semanticscholar.org/paper/f3293f326e4221f842754a62691666239e8e8fee,,semantic_scholar,,"Demonstrating how long-range entangled states are born from product states has gained much attention, which is not only important for quantum technology but also provides an unconventional tool in characterizing and classifying exotic phases of matter. In this paper, we introduce a unified and effic"
603,,"Combinatorial Cell Complexes: Duality, reconstruction and causal cobordisms",Maxime Savoy,2022,,,,,,3,0.000,0.000,,https://www.semanticscholar.org/paper/01ec853346b7a9ba0abf8d98a5907bcb119a538a,,semantic_scholar,,This thesis proposes a framework based on a notion of combinatorial cell complex (cc) whose cells are defined simply as finite sets of vertices. The cells of a cc are subject to four axioms involving a rank function that assigns a rank (or a dimension) to each cell. Our framework focuses on classes 
604,,Mathematical programming formulations for piecewise polynomial functions,B. Grimstad; B. Knudsen,2020,Journal of Global Optimization,,,,,5,0.000,0.000,10.1007/s10898-020-00881-4,https://www.semanticscholar.org/paper/67afb6ca1c18e78566e9eb220ca62ef93a9876e3,https://link.springer.com/content/pdf/10.1007/s10898-020-00881-4.pdf,semantic_scholar,,"This paper studies mathematical programming formulations for solving optimization problems with piecewise polynomial (PWP) constraints. We elaborate on suitable polynomial bases as a means of efficiently representing PWPs in mathematical programs, comparing and drawing connections between the monomi"
605,,Network controllability measures of subnetworks: implications for neurosciences,Jule E. Stocker; Erfan Nozari; Marieke K. van Vugt; A. Jansen; H. Jamalabadi,2022,bioRxiv,,,,,4,0.000,0.000,10.1088/1741-2552/acb256,https://www.semanticscholar.org/paper/4ebd644ffff1c15629755847822aec64a5ec92cc,https://pure.rug.nl/ws/files/606451182/Stocker_2023_J._Neural_Eng._20_016044.pdf,semantic_scholar,,"Objective: Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially"
606,,Revisiting Volterra defects: geometrical relation between edge dislocations and wedge disclinations,Shunsuke Kobayashi; Katsumi Takemasa; R. Tarumi,2024,Royal Society Open Science,,,,,1,0.000,0.000,10.1098/rsos.242213,https://www.semanticscholar.org/paper/d1245220b70419c82ab312c3ab0b79debf077298,,semantic_scholar,,"This study presents a comprehensive mathematical model for Volterra defects and explores their relations using differential geometry on Riemann–Cartan manifolds. Following the standard Volterra process, we derived the Cartan moving frame, a geometric representation of plastic fields, and the associa"
607,,An Open Unified Addressing System for 6G Communication Networks,Guanwen Li; D. Lou; A. Galis; Jinze Yang; Chuang Wang,2022,2022 IEEE Future Networks World Forum (FNWF),,,,,0,0.000,0.000,10.1109/FNWF55208.2022.00034,https://www.semanticscholar.org/paper/e052320b7d61e70e69f249f3052527051709a1ae,https://discovery.ucl.ac.uk/10166968/1/OUA_FNWF_WS7.pdf,semantic_scholar,,"With the rapid and continuous development of the Internet, it is foreseeable that current addressing schemes and fixed-length IP addresses would create further bottlenecks and limitations in realizing future 6G networking requirements, such as massive connections, resource-constrained communication,"
608,,Computing Topological Indices and Polynomials of the Rhenium Trioxide,S. Imran; Muhammad Mudassar Raza; N. Nigar; S. Kirmani; F. B. Petros,2022,Journal of mathematics,,,,,0,0.000,0.000,10.1155/2022/4838327,https://www.semanticscholar.org/paper/7736643e6caab370dcb1b7a3c11180870647e5a4,https://downloads.hindawi.com/journals/jmath/2022/4838327.pdf,semantic_scholar,,"In the study of mathematical chemistry and chemical graph theory, a topological index, also known as a connectivity index, is the arithmetical framework of a graph that specifies its topology and also graph invariant. These topological indices are used to model quantitative structure relationships 
"
609,,Integrated Multi-Objective Optimization for Reheating Furnace Scheduling and Rolling Plan in Hot Rolling Process of Steel Industry,Qi Wang; Zhongyang Han; Jun Zhao; Wei Wang,2022,Chinese Control and Decision Conference,,,,,3,0.000,0.000,10.1109/CCDC55256.2022.10033616,https://www.semanticscholar.org/paper/cff9df082579596d6037c1f8d252098d086eddd1,,semantic_scholar,,"As one of the most important process, hot rolling that consists of several procedures exhibits as both a primary production period and an intensive energy-consuming consumer, of which the production efficiency and energy consumption is of great significance for the enterprises. Although existed meth"
610,,Aharonov-Bohm Effects for Electromagnetism and Gravity in Four-Dimensional Spacetime,Yanhui Li; Y. Reyimuaji,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/75a4adba8e0d9bfff240eb167b97e05d2a47408a,,semantic_scholar,,"This paper investigates a geometric framework for the gravitational Aharonov-Bohm effect in four-dimensional spacetime, demonstrating how spacetime curvature induces nonlocal quantum phase shifts within field-free regions. By constructing vector bundles on spacetime manifolds equipped with Levi-Civi"
611,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25065v1,https://arxiv.org/pdf/2512.25065v1,arxiv,,"Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously g"
612,,Feeling Blue: Constructing a Robust SALT3 UV Template and Constraining its Redshift Dependency,Qinan Wang; David O. Jones; Justin D. R. Pierel; Matthew R. Siebert; W. D'Arcy Kenworthy,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25064v1,https://arxiv.org/pdf/2512.25064v1,arxiv,,"Upcoming cosmological surveys will obtain numerous rest-frame ultraviolet (UV) observations of Type Ia supernovae (SNe Ia), yet there is concern about how standardizable SNe Ia are in the UV. In this work, we train a robust optical--UV SED model for SNe Ia (SALT3-UV) with the open-source model-train"
613,,Numerical study of solitary waves in Dirac--Klein--Gordon system,Andrew Comech; Julien Ricaud; Marco Roque,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24954v1,https://arxiv.org/pdf/2512.24954v1,arxiv,,"We use numerics to construct solitary waves in Dirac--Klein--Gordon (in one and three spatial dimensions) and study the dependence of energy and charge on $ω$. For the construction, we use the iterative procedure, starting from solitary waves of nonlinear Dirac equation, computing the corresponding "
614,,Dynamic response phenotypes and model discrimination in systems and synthetic biology,Eduardo D. Sontag,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24945v1,https://arxiv.org/pdf/2512.24945v1,arxiv,,"Biological systems encode function not primarily in steady states, but in the structure of transient responses elicited by time-varying stimuli. Overshoots, biphasic dynamics, adaptation kinetics, fold-change detection, entrainment, and cumulative exposure effects often determine phenotypic outcomes"
615,,Securing High-Concurrency Ticket Sales: A Framework Based on Microservice,Zhiyong Zhang; Xiaoyan Zhang; Xiaoqi Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24941v1,https://arxiv.org/pdf/2512.24941v1,arxiv,,"The railway ticketing system is one of the most important public service infrastructure. In peak periods such as holidays, it is often faced with the challenge of high concurrency scenarios because of a large number of users accessing at the same time. The traditional aggregation architecture can no"
616,,Constraints on the perfect phylogeny mixture model and their effect on reducing degeneracy,John Marangola; Azadeh Sheikholeslami; José Bento,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24930v1,https://arxiv.org/pdf/2512.24930v1,arxiv,,"The perfect phylogeny mixture (PPM) model is useful due to its simplicity and applicability in scenarios where mutations can be assumed to accumulate monotonically over time. It is the underlying model in many tools that have been used, for example, to infer phylogenetic trees for tumor evolution an"
617,,Cosmological dynamics and observational constraints of an interacting early scalar field coupled to radiation,Dorian Araya; Felipe Herrera; Nelson Videla,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24918v1,https://arxiv.org/pdf/2512.24918v1,arxiv,,"We study the cosmic evolution of an interacting scalar field radiation model, in which a minimally coupled scalar field exchanges energy with the radiation sector through an exponential coupling. Extending previous formulations, a non-relativistic matter component is included explicitly, which allow"
618,,Stochastic factors can matter: improving robust growth under ergodicity,Balint Binkert; David Itkin; Paul Mangers Bastian; Josef Teichmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24906v1,https://arxiv.org/pdf/2512.24906v1,arxiv,,"Drifts of asset returns are notoriously difficult to model accurately and, yet, trading strategies obtained from portfolio optimization are very sensitive to them. To mitigate this well-known phenomenon we study robust growth-optimization in a high-dimensional incomplete market under drift uncertain"
619,,One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication,Yufan Lin; Xavier Guidetti; Yannick Nagel; Efe C. Balta; John Lygeros,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24905v1,https://arxiv.org/pdf/2512.24905v1,arxiv,,"Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between printhead motion and material extrusion systems, notably corner over-extrusion. Existing methods require specialized hard"
620,,MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy,Chang Liu; Junzhou Zhao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24899v1,https://arxiv.org/pdf/2512.24899v1,arxiv,,"The proliferation of streaming data analytics in data-driven applications raises critical privacy concerns, as directly collecting user data may compromise personal privacy. Although existing $w$-event local differential privacy (LDP) mechanisms provide formal guarantees without relying on trusted t"
621,,"Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy",Anantha S. Rao; Barnaby van Straaten; Valentin John; Cécile X. Yu; Stefan D. Oosterhout,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24894v1,https://arxiv.org/pdf/2512.24894v1,arxiv,,"The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a "
622,,Adaptive Clutter Suppression via Convex Optimization,Yifan He; Griffin Kearney; Makan Fardad,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24889v1,https://arxiv.org/pdf/2512.24889v1,arxiv,,Passive and bistatic radar systems are often limited by strong clutter and direct-path interference that mask weak moving targets. Conventional cancellation methods such as the extensive cancellation algorithm require careful tuning and can distort the delay-Doppler response. This paper introduces a
623,,SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance,Qian'ang Mao; Jiaxin Wang; Ya Liu; Li Zhu; Jiaman Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24888v1,https://arxiv.org/pdf/2512.24888v1,arxiv,,The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudon
624,,A structure-preserving parametric approximation for anisotropic geometric flows via an $α$-surface energy matrix,Weizhu Bao; Yifei Li; Wenjun Ying; Yulin Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24875v1,https://arxiv.org/pdf/2512.24875v1,arxiv,,"We propose a structure-preserving parametric approximation for geometric flows with general anisotropic effects. By introducing a hyperparameter $α$, we construct a unified surface energy matrix $\hat{\boldsymbol{G}}_k^α(θ)$ that encompasses all existing formulations of surface energy matrices, and "
625,,Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements,Yiming Liang; Yizhi Li; Yantao Du; Ge Zhang; Jiayi Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24867v1,https://arxiv.org/pdf/2512.24867v1,arxiv,,"Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predominantly curate questions at the question level, suffering from three fundamental limitations: vulnerability to data contami"
626,,Latent Twins: A Framework for Scene Recognition and Fast Radiative Transfer Inversion in FORUM All-Sky Observations,Cristina Sgattoni; Luca Sgheri; Matthias Chung; Michele Martinazzo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24865v1,https://arxiv.org/pdf/2512.24865v1,arxiv,,"The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing radiation, enabling improved understanding of atmospheric processes and the radiation budget. Retrieving atmospheric st"
627,,"Antecedents of Consumer Regret Frequency: The Roles of Decision Agency, Status Signaling, and Online Shopping Preference",Shawn Berry,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24862v1,https://arxiv.org/pdf/2512.24862v1,arxiv,,"Consumer regret is a widespread post-purchase emotion that significantly impacts satisfaction, product returns, complaint behavior, and customer loyalty. Despite its prevalence, there is a limited understanding of why certain consumers experience regret more frequently as a chronic aspect of their e"
628,,OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation,Meng Lan; Lefei Zhang; Xiaomeng Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24861v1,https://arxiv.org/pdf/2512.24861v1,arxiv,,"The Segment Anything Model 2 (SAM2) has demonstrated remarkable promptable visual segmentation capabilities in video data, showing potential for extension to medical image segmentation (MIS) tasks involving 3D volumes and temporally correlated 2D image sequences. However, adapting SAM2 to MIS presen"
629,,VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents,Xunyi Zhao; Gengze Zhou; Qi Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24851v1,https://arxiv.org/pdf/2512.24851v1,arxiv,,"Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, which requires multi-round dialogue spatial reasoning and sequential action prediction, needs further exploration. Our work "
630,,AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference,Linhao Fan; Hongqiang Fang; Jingyang Dai; Yong Jiang; Qixing Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24847v1,https://arxiv.org/pdf/2512.24847v1,arxiv,,"High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstr"
631,,All optical Lithography for Spatiotemporal Patterning of Azopolymer Microreliefs,I Komang Januariyasa; Francesco Reda; Nikolai Liubimtsev; Marina Saphiannikova; Fabio Borbone,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25048v1,https://arxiv.org/pdf/2512.25048v1,arxiv,,"Microstructured surfaces are central to photonics, biointerfaces, and functional coatings, yet they are typically fabricated through multi-step lithographic workflows requiring masks or molds and post-processing. Azopolymers provide an alternative route by converting structured optical fields into s"
632,,Universal polar dual pairs of spherical codes found in $E_8$ and $Λ_{24}$,S. V. Borodachov; P. G. Boyvalenkov; P. D. Dragnev; D. P. Hardin; E. B. Saff,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25037v1,https://arxiv.org/pdf/2512.25037v1,arxiv,,"We identify universal polar dual pairs of spherical codes $C$ and $D$ such that for a large class of potential functions $h$ the minima of the discrete $h$-potential of $C$ on the sphere occur at the points of $D$ and vice versa. Moreover, the minimal values of their normalized potentials are equal."
633,,Strengthening Dual Bounds for Multicommodity Capacitated Network Design with Unsplittable Flow Constraints,Lacy M. Greening; Santanu S. Dey; Alan L. Erera,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25018v1,https://arxiv.org/pdf/2512.25018v1,arxiv,,"Multicommodity capacitated network design (MCND) models can be used to optimize the consolidation of shipments within e-commerce fulfillment networks. In practice, fulfillment networks require that shipments with the same origin and destination follow the same transfer path. This unsplittable flow r"
634,,Noise resilient real-time phase imaging via undetected light,Josué R. León-Torres; Patrick Hendra; Yugant Mukeshbhai Hadiyal; Christopher Spiess; Fabian Steinlechner,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24993v1,https://arxiv.org/pdf/2512.24993v1,arxiv,,Quantum imaging with undetected light has recently emerged as a technique in which quantum correlations and nonlinear interferometry are combined to decouple illumination and detection paths. This approach has been more recently extended and combined with digital phase-shifting holography and off-ax
635,,A guide to the $2$-generated axial algebras of Monster type,Justin McInroy; Abdul Wajid Mir,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24987v1,https://arxiv.org/pdf/2512.24987v1,arxiv,,"Axial algebras of Monster type are a class of non-associative algebras which generalise the Griess algebra, whose automorphism group is the largest sporadic simple group, the Monster. The $2$-generated algebras, which are the building blocks from which all algebras in this class can be constructed, "
636,,Any Clifford+T circuit can be controlled with constant T-depth overhead,Isaac H. Kim; Tuomas Laakkonen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24982v1,https://arxiv.org/pdf/2512.24982v1,arxiv,,"Since an n-qubit circuit consisting of CNOT gates can have up to $Ω(n^2/\log{n})$ CNOT gates, it is natural to expect that $Ω(n^2/\log{n})$ Toffoli gates are needed to apply a controlled version of such a circuit. We show that the Toffoli count can be reduced to at most n. The Toffoli depth can also"
637,,ShowUI-$π$: Flow-based Generative Models as GUI Dexterous Hands,Siyuan Hu; Kevin Qinghong Lin; Mike Zheng Shou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24965v1,https://arxiv.org/pdf/2512.24965v1,arxiv,,"Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progre"
638,,High-performance quantum interconnect between bosonic modules beyond transmission loss constraints,Hongwei Huang; Jie Zhou; Weizhou Cai; Weiting Wang; Yilong Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24926v1,https://arxiv.org/pdf/2512.24926v1,arxiv,,"Distributed quantum computing architectures require high-performance quantum interconnects between quantum information processing units, while previous implementations have been fundamentally limited by transmission line losses. Here, we demonstrate a low-loss interconnect between two superconductin"
639,,Adaptive Resource Orchestration for Distributed Quantum Computing Systems,Kuan-Cheng Chen; Felix Burt; Nitish K. Panigrahy; Kin K. Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24902v1,https://arxiv.org/pdf/2512.24902v1,arxiv,,Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent quantum-HPC system. We propose the Modular Entanglement Hub (ModEn-Hub) architecture: a hub-and-spoke photonic interconnect paired with a real-time quantum network orchestrator. 
640,,Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing,Andrii Gamalii; Daniel Górniak; Robert Nowak; Bartłomiej Olber; Krystian Radlak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24896v1,https://arxiv.org/pdf/2512.24896v1,arxiv,,"This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimodal dataset of driving scenarios recorded in Polish conditions. Manual annotation of such heterogeneous data is both costl"
641,,Feature Slice Matching for Precise Bug Detection,Ke Ma; Jianjun Huang; Wei You; Bin Liang; Jingzheng Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24858v1,https://arxiv.org/pdf/2512.24858v1,arxiv,,"Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the noise interference in existing works does not manage the tough job, i.e., eliminating the noise in the targets. In this pa"
642,,friends.test: rank-based method for feature selection in interaction matrices,Alexandra Suvorikova; Alexey Kroshnin; Dmirijs Lvovs; Vera Mukhina; Andrey Mironov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24843v1,https://arxiv.org/pdf/2512.24843v1,arxiv,,"The analysis of the interaction matrix between two distinct sets is essential across diverse fields, from pharmacovigilance to transcriptomics. Not all interactions are equally informative: a marker gene associated with a few specific biological processes is more informative than a highly expressed "
643,,Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration,Jun-Chao Liang; Yin-Bi Li; A-Li Luo; Fang Zuo; Bing Du,2025,arXiv,,,,,0,0.000,0.000,10.3847/1538-4357/ae1446,http://arxiv.org/abs/2512.24840v1,https://arxiv.org/pdf/2512.24840v1,arxiv,,"To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented"
644,,Non-Abelian Geometric Phases in Triangular Structures And Universal SU(2) Control in Shape Space,J. Dai; A. Molochkov; A. J. Niemi; J. Westerholm,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24798v1,https://arxiv.org/pdf/2512.24798v1,arxiv,,"We construct holonomic quantum gates for qubits that are encoded in the near-degenerate vibrational $E$-doublet of a deformable three-body system. Using Kendall's shape theory, we derive the Wilczek--Zee connection governing adiabatic transport within the $E$-manifold. We show that its restricted ho"
645,,Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training,Andrew Tinits; Stephen Mann,2025,arXiv,,,,,0,0.000,0.000,10.1145/3757377.3763931,http://arxiv.org/abs/2512.24794v1,https://arxiv.org/pdf/2512.24794v1,arxiv,,"The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major lim"
646,,Digitalizing Over-the-Air Computation via The Novel Complement Coded Modulation,Zhixu Wang; Jiacheng Yao; Wei Xu; Wei Shi; Kaibin Huang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24788v1,https://arxiv.org/pdf/2512.24788v1,arxiv,,"To overcome inherent limitations of analog signals in over-the-air computation (AirComp), this letter proposes a two's complement-based coding scheme for the AirComp implementation with compatible digital modulations. Specifically, quantized discrete values are encoded into binary sequences using th"
647,,Runaway electron avalanche and macroscopic beam formation: simulations of the DTT full power scenario,E. Emanuelli; F. Vannini; M. Hoelzl; E. Nardon; V. Bandaru,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24760v1,https://arxiv.org/pdf/2512.24760v1,arxiv,,"The transition of the Divertor Tokamak Test (DTT) facility from its initial commissioning phase (Day-0, plasma current $I_{p}=2$ MA) to the full power scenario ($I_{p}=5.5$ MA) introduces a critical shift in the dynamics of runaway electrons (REs) generation. While previous predictive studies of the"
648,,Easier randomizing gates provide more accurate fidelity estimation,Debankan Sannamoth; Kristine Boone; Arnaud Carignan-Dugas; Akel Hashim; Irfan Siddiqi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24744v1,https://arxiv.org/pdf/2512.24744v1,arxiv,,"Accurate benchmarking of quantum gates is crucial for understanding and enhancing the performance of quantum hardware. A standard method for this is interleaved benchmarking, a technique which estimates the error on an interleaved target gate by comparing cumulative error rates of randomized sequenc"
649,,S-Duality for Non-Abelian Monopoles,Shan Hu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24743v1,https://arxiv.org/pdf/2512.24743v1,arxiv,,"In $\mathcal{N}=4$ super-Yang-Mills theory with gauge group $G$ spontaneously broken to a subgroup $H$, S-duality requires that the BPS monopole spectrum organizes into the same representation as W-bosons in the dual theory, where $G^{\vee}$ is broken to $H^{\vee}$. The expectation has been extensiv"
650,,Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression,Xiang Liu; Yimin Zhou; Jinxiang Wang; Yujun Huang; Shuzhao Xie,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24742v1,https://arxiv.org/pdf/2512.24742v1,arxiv,,"The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Exist"
651,,Model-independent search of gravitational wave echoes in LVK data,Di Wu; Xi-Li Zhang; Qing-Guo Huang; Jing Ren,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24730v1,https://arxiv.org/pdf/2512.24730v1,arxiv,,"Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard ''black hole spectroscopy''. Theoretical waveform predictions, however, remain uncertain, motivating robust searches that avoid specific echo modeling. We present a model-in"
652,,Phase transitions in time complexity of Brownian circuits,Kota Okajima; Koji Hukushima,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24728v1,https://arxiv.org/pdf/2512.24728v1,arxiv,,"Brownian circuits implement computation through stochastic transitions driven by thermal fluctuations. While the energetic costs of such fluctuation-driven computation have been extensively studied within stochastic thermodynamics, much less is known about its computational complexity, in particular"
653,,Equivalence of Personalized PageRank and Successor Representations,Beren Millidge,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24722v1,https://arxiv.org/pdf/2512.24722v1,arxiv,,"The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each ind"
654,,Primordial black hole dark matter from ultra-slow-roll inflation in Horndeski gravity,Despina Totolou; Theodoros Papanikolaou; Emmanuel N. Saridakis,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25044v1,https://arxiv.org/pdf/2512.25044v1,arxiv,,"Primordial black holes (PBHs) provide a well-motivated non-particle candidate for dark matter, requiring an enhancement of curvature perturbations on small inflationary scales consistent with observational constraints. In this work we study PBH production within Horndeski gravity, accounting for com"
655,,Towards precision cosmology with Voids x CMB correlations (I): Roman-Agora mock catalogs and pipeline validation,Mar Pérez Sar; Carlos Hernández Monteagudo; András Kovács; Alice Pisani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25040v1,https://arxiv.org/pdf/2512.25040v1,arxiv,,"We construct and validate a set of multi-purpose mock galaxy catalogs designed to capture, to different degrees of accuracy, the main characteristics of the Nancy Grace Roman Space Telescope survey. These catalogs provide a foundation for void statistics and various CMB cross-correlation analyses. O"
656,,Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information,Ameet Gadekar; Aristides Gionis; Suhas Thejaswi; Sijing Tu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24934v1,https://arxiv.org/pdf/2512.24934v1,arxiv,,"We study the problem of fair $k$-committee selection under an egalitarian objective. Given $n$ agents partitioned into $m$ groups (\eg, demographic quotas), the goal is to aggregate their preferences to form a committee of size $k$ that guarantees minimum representation from each group while minimiz"
657,,Single Phase Immersion Cooling for Hyper Scale Data Centers: Challenges and Opportunities,D. Agonafer; P. Bansode; S. Saini; J. Gullbrand; Ashish Gupta,2023,ASME 2023 Heat Transfer Summer Conference,,,,,3,0.000,0.000,10.1115/ht2023-107598,https://www.semanticscholar.org/paper/38d1cd159f40d43683c2380abae8cd82d15a4465,,semantic_scholar,,"
 Rapidly expanding computing, storage, and networking requirements have increased the quantity and energy density of modern data centers. Air-cooled high-performance servers often require low air-supply temperatures as well as high air-flow rates, making air-cooling inefficient above certain therma"
658,,Logarithmic Connections on Principal Bundles and Their Applications to Geometric Control Theory,Álvaro Antón‐Sancho,2025,Axioms,,,,,0,0.000,0.000,10.3390/axioms15010010,https://www.semanticscholar.org/paper/cd7b2a29f28bf5b52ca25ed9e37466cd5a44fad2,,semantic_scholar,,"In this research, we establish a precise correspondence between the theory of logarithmic connections on principal G-bundles over compact Riemann surfaces and the geometric formulation of control systems on curved manifolds, providing a novel differential–geometric framework for analyzing optimal co"
659,,AutoMap: Automatic Mapping of Neural Networks to Deep Learning Accelerators for Edge Devices,Yanhong Wang; Zihao Zhao; Xu Jin; Haotian Zheng; Maohua Nie,2023,IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,,,,,4,0.000,0.000,10.1109/TCAD.2022.3232070,https://www.semanticscholar.org/paper/b7bf209183e78a4e8253f79b95847dece9925401,,semantic_scholar,,"Emerging deep neural networks (DNNs) have been emerging in applications (object detection, automatic speech recognition, etc.) deployed on edge devices. To improve the energy efficiency of edge devices, domain-specific deep learning accelerators (DLAs) are designed with limited on-chip resources. Th"
660,,Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences,Zhaoze Wang; Ronald W. Di Tullio; Spencer Rooke; Vijay Balasubramanian,2024,Neural Information Processing Systems,,,,,9,0.000,0.000,10.48550/arXiv.2408.05798,https://www.semanticscholar.org/paper/2c63b474de44a4982b68b8d321427ebef6f85574,,semantic_scholar,,"The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps supporting spatial memory. Here we show that place cells emerge in ne"
661,,Riemannian Bilevel Optimization,Sanchayan Dutta; Xiang Cheng; S. Sra,2024,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2405.15816,https://www.semanticscholar.org/paper/45a2b6a8621acb3a2f1446c1cd8e56180cca1e44,,semantic_scholar,,"We develop new algorithms for Riemannian bilevel optimization. We focus in particular on batch and stochastic gradient-based methods, with the explicit goal of avoiding second-order information such as Riemannian hyper-gradients. We propose and analyze $\mathrm{RF^2SA}$, a method that leverages firs"
662,,A note on the Hamiltonian structure of transgression forms,P. Pais; Patricio Salgado-Rebolledo; Aldo Vera,2023,Journal of High Energy Physics,,,,,0,0.000,0.000,10.1007/JHEP12(2023)190,https://www.semanticscholar.org/paper/b10b717502df0fae1fc8bc71e4a13c5398944109,https://link.springer.com/content/pdf/10.1007/JHEP12(2023)190.pdf,semantic_scholar,,"By incorporating two gauge connections, transgression forms provide a generalization of Chern-Simons actions that are genuinely gauge-invariant on bounded manifolds. In this work, we show that, when defined on a manifold with a boundary, the Hamiltonian formulation of a transgression field theory ca"
663,,A Novel Dynamic Hybrid Beamforming Design for ELAA Systems,Meng-Ting Liu; Ming Li; Rang Liu; Qian Liu,2024,ICC 2024 - IEEE International Conference on Communications,,,,,1,0.000,0.000,10.1109/ICC51166.2024.10622286,https://www.semanticscholar.org/paper/185bbc06d2122e14ca4d5d72908ddf708ddd4e4d,,semantic_scholar,,"Extremely large-scale antenna array (ELAA) is deemed as one of several key candidate technologies for the sixth generation (6G) mobile networks. Nevertheless, the near-field effect poses a significant challenge for ELAA systems as a result of employing a substantial quantity of antennas for the tran"
664,,"GNPHE / 03-04 hep-th / 0303198 M-theory on G 2 manifolds and the method of ( p , q ) brane webs",A. Belhaj,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/dde5ad445fcd6894ccddaa7c3ebe9f02420939f9,,semantic_scholar,,
665,,Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest,Saeed Ebrahimi; Weijie Jiang; Jaewon Yang; Olafur Gudmundsson; Yucheng Tu,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/a2dcad40027095c84a3d570f9fe6ccbbcdb6af86,,semantic_scholar,,"Pinterest is a leading visual discovery platform where recommender systems (RecSys) are key to delivering relevant, engaging, and fresh content to our users. In this paper, we study the problem of improving RecSys model predictions for cold-start (CS) items, which appear infrequently in the training"
666,,Digital Innovation of Well Construction Process in Ecuador Through Rig Automation,Karen Peña; Kevin Etcheverry; Hugo Quevedo; Esteban Rojas; R. Correa,2023,"Day 2 Tue, October 03, 2023",,,,,1,0.000,0.000,10.2118/216249-ms,https://www.semanticscholar.org/paper/f89e3831b1e947962f0f0e1e241737e84975f540,,semantic_scholar,,"
 Artificial intelligence-based (AI) digital drilling technology was implemented in two mature fields of Ecuador, which represent 33% of the country's oil production and where it is essential to maximize the return on investment. The drilling campaign's main strategy included the deployment of a nov"
667,,Combinatorial decompositions for deformed or decorated classes of maps,V. Nador,2023,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/e08cf8185b2aedcef2858e849df7737ef10c157e,,semantic_scholar,,The perturbative expansion of tensorial field theories in Feynman graphs can be interpreted as weighted generating series of some piecewise linear varieties. This simple fact establishes a link between two a priori distinct fields: the combinatorics of discrete manifolds on one hand and tensorial fi
668,,"on Matter under Extreme Conditions in Solar System Giant Planets and Exoplanets, Inverse Problems and Deep Learning",,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/d6d265d2ea2ff8c251923f3c871a15a51ee53623,,semantic_scholar,,
669,,Optimal Mass Transport Meets Thermodynamics: On Power and Efficiency of Finite-Time Thermodynamic Engines,Huidong Chen,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/a3ebed9b1e75a528f8e943275bdb99ffe515ab2c,,semantic_scholar,,
670,,Projects with allocated PhD studentships Algorithms and Data Analysis,,2021,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/0f61d7b297f3c6c3652627c8f1d68f5f32afd634,,semantic_scholar,,
671,,Team Apics Analysis and Problems of Inverse type in Control and Signal processing,S. Antipolis,2021,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/f6389938d5cc150e359aee87d41e2dd2135a43c8,,semantic_scholar,,