Geometric Representation Learning

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28 papers in the last four weeks, up 155% on the four weeks before. 0.3% of all new papers.

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Learning Graph Foundation Models on Riemannian Graph-of-Graphs
May 11, 2026Haokun Liu, Zezhong Ding, Xike XieGraph Representation Learning Domain Generalization
School of Biomedical Engineering, University of Science and Technology of China (USTC), Suzhou, Jiangsu, China · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China · School of Artificial Intelligence and Data Science, USTC, Hefei, Anhui, China
An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories
May 10, 2026Arafat Rahman, Shashwat Kumar, Laura E. Barnes +1Variational Autoencoders Skeleton-Based Action Recognition
Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA · Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA · Dept. of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
Structure-Centric Graph Foundation Model via Geometric Bases
May 9, 2026Xiaodong He, Haolan He, Ruiyi Fang +2Geometric Representation Learning Graph Foundation Models
University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China · Western University
MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching
May 8, 2026Salim Khazem, Ibrahim Mohamed Serouis, Zakaria EzzahedFlow Matching Vision Foundation Model Adaptation
Talan Research Center, Paris, France
jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers
May 8, 2026Florian Hönicke, Michael Günther, Andreas Koukounas +4Cross-Modal Learning Cross-Modal Representation Learning
Jina by Elastic
Toward Better Geometric Representations for Molecule Generative Models
May 8, 2026Shaoheng Yan, Zian Li, Cai Zhou +3Geometric Representation Learning 3D Molecular Generation
Institute for Artificial Intelligence, PKU · ByteDance AI Drug Discovery · School of Intelligence Science and Technology, PKU +3
A Generalized Singular Value Theory for Neural Networks
May 7, 2026Brian Charles Brown, Robert Bridges, David Grimsman +2Neural Representation Geometry Geometric Representation Learning
AI Sweden Gothenburg, Sweden
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
May 7, 2026Kartik Tandon, Julian Gould, Tanishq Bhatia +3Graph Neural Networks Geometric Representation Learning
University of Pennsylvania · Sakana AI · Northeastern University +1
Geometry-Aware Simplicial Message Passing
May 7, 2026Elena Xinyi Wang, Bastian RieckMessage Passing Neural Networks Geometric Representation Learning
AIDOS Lab, University of Fribourg Fribourg, Switzerland
Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking
May 6, 2026Kyungwon Jeong, Won-Gi Paeng, Honggyo SuhSymmetry Breaking Geometric Representation Learning
Hyntel
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
May 6, 2026Guoze Sun, Tianya Miao, Haoyang Huang +4Neural Surrogate Modeling Geometric Representation Learning
Gaoling School of Artificial Intelligence, Renmin University of China
Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning
May 6, 2026Tushar Das, Subrata Dutta, Sarmistha Neogy +1Geometric Representation Learning Metric Learning
National Institute of Technology Jamshedpur · Jadavpur University · Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur
A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning
May 5, 2026Alexandre L. M. LevadaGeometric Representation Learning
Federal University of São Carlos 13565-905, São Carlos-SP, Brazil
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
May 3, 2026Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2Alzheimer's Disease Medical Imaging Foundation Models
Stanford University · Stanford University, CA
Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
May 3, 2026Yifan Li, Arravind Subramanian, Xiaoqing Liu +3Geometric Representation Learning Cross-Domain Transfer Learning
Department of Mechanical Engineering,National University of Singapore, 117575, SG · Department of Chemical and Biomolecular Engineering,National University of Singapore, 117575, SG
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Apr 30, 2026Sahil Mishra, Srinitish Srinivasan, Sourish Dasgupta +1Hierarchical Classification Hierarchical Representation Learning
Indian Institute of Technology Delhi, New Delhi, India · KDM Lab, Dhirubhai Ambani University, Gandhinagar, Gujarat, India · Indian Institute of Technology Delhi, Abu Dhabi, UAE.
Beyond Gaussian Bottlenecks: Topologically Aligned Encoding of Vision-Transformer Feature Spaces
Apr 30, 2026Andrew Bond, Ilkin Umut Melanlioglu, Erkut Erdem +1Variational Autoencoders Hyperspherical Representation Learning
Department of Computer Engineering, Kocı University, Istanbul, Turkey · KUIS AI Research Center, Istanbul, Turkey · Department of Electrical and Electronics Engineering, Kocı University, Istanbul, Turkey +1
Hyperspherical Forward-Forward with Prototypical Representations
Apr 30, 2026Shalini Sarode, Brian Moser, Joachim Folz +4Prototype-Based Learning Efficient Neural Network Inference
German Research Center for Artificial Intelligence (DFKI) · RPTU Kaiserslautern-Landau
Last-Layer-Centric Feature Recombination: Unleashing 3D Geometric Knowledge in DINOv3 for Monocular Depth Estimation
Apr 29, 2026Gongshu Wang, Zhirui Wang, Kan YangMonocular Depth Estimation Multi-Scale Feature Fusion
Aerospace Information Research Institute Chinese Academy of Sciences Beijing, China
Toward a Functional Geometric Algebra for Natural Language Semantics
Apr 28, 2026James PustejovskyGeometric Representation Learning
Computer Science Department Brandeis University
Self-Supervised Representation Learning via Hyperspherical Density Shaping
Apr 27, 2026Esteban Rodríguez-Betancourt, Edgar Casasola-MurilloMulti-View Learning Hyperspherical Representation Learning
Posgrado en Computación e Informática · Universidad de Costa Rica · Escuela de Ciencias de la Computación
GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
Apr 27, 2026Yue Min, Ziyun Qiao, Ruining Chen +1Language Model Pretraining Training Data Curation
Wizard Quant, Beijing, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China · Peking University, Beijing, China +1
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
Apr 22, 2026Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza AminiRepresentation Learning Geometric Representation Learning
Université Grenoble Alpes, CNRS, Computer Science Laboratory LIG, Grenoble, France
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Apr 22, 2026Yuhan Peng, Junwen Dong, Yuzhi Zeng +6Graph Neural Networks Geometric Representation Learning
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore · Chern Institute of Mathematics, Nankai University, Tianjin, China · Department of Mathematics, Faculty of Science, National University of Singapore, Singapore +3
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Apr 21, 2026Shijie Wang, Yadan Luo, Zijian Wang +3Fine-Grained Image Classification Geometric Representation Learning
College of Computer Science and Engineering, Shandong University of Science and Technology, China · School of Information Technology and Electrical Engineering, The University of Queensland, Australia
CCAR: Intrinsic Robustness as an Emergent Geometric Property
Apr 18, 2026Akash Samanta, Manish Pratap Singh, Debasis ChaudhuriDisentangled Representation Learning Representation Learning
Techno India University, Salt Lake, Kolkata, India · DRDO Young Scientist Laboratory - CT, Chennai, India
STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing
Apr 16, 2026Shen Fan, Mikołaj Kida, Przemyslaw MusialskiGeometric Representation Learning 3D Instance Segmentation
New Jersey Institute of Technology · Warsaw University of Technology
SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization
Mar 13, 2026Tianwei Ye, Xiaoguang Mei, Yifan Xia +3Geometric Representation Learning Non-Rigid Shape Correspondence
Wuhan University, Wuhan 430072, China
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
Feb 27, 2026Arnas Uselis, Andrea Dittadi, Seong Joon OhNeural Representation Geometry Visual Representation Learning
Tübingen AI Center, University of Tübingen · Helmholtz · Technical University of Munich
Geometry-Preserving Neural Architectures on Manifolds with Boundary
Feb 3, 2026Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum +4Geometric Representation Learning Neural Network Approximation Theory
Los Alamos National Laboratory · Boston College

May 11, 2026, cs.LGJ/K move · Enter open · S save

Learning Graph Foundation Models on Riemannian Graph-of-Graphs

Haokun Liu, Zezhong Ding, Xike Xie

School of Biomedical Engineering, University of Science and Technology of China (USTC), Suzhou, Jiangsu, China · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China · School of Artificial Intelligence and Data Science, USTC, Hefei, Anhui, China

Graph Representation Learning Domain Generalization Geometric Representation Learning Graph Foundation Models

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Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across diverse graph tasks and domains. Existing GFMs pretrained with fixed-hop subgraph sampling impose a fixed receptive field, causing scale mismatch on diverse tasks, which often require heterogeneous and unknown structural contexts beyond a fixed sampling scale. We propose R-GFM, a Riemannian Graph-of-Graphs (GoG) based foundation model, that treats structural scale as a first-class citizen in modeling. R-GFM constructs a multi-scale GoG over-sampled subgraphs at different hop distances and learns geometry-adaptive representations from Riemannian manifolds. Theoretical analysis shows that R-GFM reduces structural domain generalization error compared to fixed-scale GFMs. Experiments on various datasets demonstrate that R-GFM achieves state-of-the-art performance, with up to a 49% relative improvement on downstream tasks. Our code is available at https://github.com/USTC-DataDarknessLab/R-GFM.

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