Invariant Representation Learning

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Invariant Features in Language Models: Geometric Characterization and Model Attribution
May 7, 2026Agnibh Dasgupta, Abdullah Tanvir, Xin ZhongNeural Representation Geometry Invariant Representation Learning
Department of Computer Science University of Nebraska Omaha, Omaha, NE, USA
Adaptive Physical-Facial Representation Fusion via Subject-Invariant Cross-Modal Prompt Tuning for Video-Based Emotion Recognition
May 7, 2026Xiwen Luo, Jia Li, Rencheng Song +2Multimodal Emotion Recognition Invariant Representation Learning
Department of Biomedical Engineering, and Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei, 230009 China · School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601 China
CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization
May 6, 2026Yu-Hsi Chen, Abd-Krim SeghouanePrincipal Component Analysis Domain Generalization
The University of Melbourne
Bridging Input Feature Spaces Towards Graph Foundation Models
May 6, 2026Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua +2Graph Representation Learning Transfer Learning
University of Cambridge · Ben-Gurion University of the Negev · Purdue University
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
May 6, 2026Zhenchao Sun, Shuai Ma, Ping Lu +1Hypergraph Neural Networks Constraint Satisfaction
SKLCCSE Lab, Beihang University, Beijing, China.
MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation
May 2, 2026Ahmed Y. Radwan, Hina TabassumSource-Free Domain Adaptation Human Activity Recognition
department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada
Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs
Apr 30, 2026Nuria Alabau-Bosque, Jorge Vila-Tomas, Paula Dauden-Oliver +2Neural Network Robustness Convolutional Neural Networks
Image Processing Lab, Universitat de València, Carrer del Catedr´atic Jos´e Beltr´an Martinez, Paterna, 46980, Spain.
Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition
Apr 29, 2026Jiangtao Fan, Anish Jindal, Amir Atapour-AbarghoueiHuman Activity Recognition Wearable Sensing
Department of Computer Science, Durham University, Durham, DH1 3LE, UK
Robust Representation Learning through Explicit Environment Modeling
Apr 28, 2026Yuli Slavutsky, David M. BleiRepresentation Learning Domain Generalization
Department of Statistics Columbia University · Departments of Statistics and Computer Science Columbia University
ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching
Apr 27, 2026Jong Woo Nam, Amanda S. Rios, Bartlett W. MelInvariant Representation Learning Fine-Grained Visual Perception
Probing CLIP's Comprehension of 360-Degree Textual and Visual Semantics
Apr 27, 2026Hai Wang, Xiaochen Yang, Mingzhi Dong +1VLM Evaluation VLM Adaptation
Department of Statistical Science, University College London, UK · School of Mathematics and Statistics, University of Glasgow, UK · Department of Computer Science, University of Bath, UK.
Improving Robustness of Tabular Retrieval via Representational Stability
Apr 27, 2026Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao +2Retrieval Robustness Information Retrieval
Department of Computer Science Arizona State University Tempe, Arizona, USA · ScaleAI San Francisco, CA, USA
Magnetic Indoor Localization through CNN Regression and Rotation Invariance
Apr 24, 2026Helge Rosé, Konstantin Klipp, Tom Koubek +2GNSS-Denied Localization Convolutional Neural Networks
Fraunhofer Institute for Open Communication Systems (FOKUS) Berlin, Germany · Daimler Center for Automotive Information Technology Innovations (DCAITI) Technical University Berlin Berlin, Germany
Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification
Apr 21, 2026Xudong Jian, Charikleia Stoura, Simon Scandella +1Disentangled Representation Learning Self-Supervised Time Series Representation Learning
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland. · Department of Mechanical Engineering, Politecnico di Milano, Italy
Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
Apr 20, 2026Sijie Mai, Shiqin HanDisentangled Representation Learning Causal Representation Learning
School of Computer Science, South China Normal University
Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels
Mar 31, 2026Marcel Tomàs Bernal, Neil Rohit Mallinar, Mikhail BelkinSymmetry Breaking Kernel Methods
CFIS UPC · UC San Diego
On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Mar 29, 2026Mohammad Tinati, Stephen TuSelf-Supervised Pre-Training Invariant Representation Learning
Department of Electrical and Computer Engineering, University of Southern California
When Sensing Varies with Contexts: Context Probing for Tactile Few-Shot Class-Incremental Learning
Mar 26, 2026Yifeng Lin, Aiping Huang, Wenxi Liu +4Class-Incremental Learning Prototype-Based Classification
Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
TransMASK: Masked State Representation through Learned Transformation
Mar 5, 2026Sagar Parekh, Preston Culbertson, Dylan P. LoseyRepresentation Learning Robot Imitation Learning
Mechanical Engineering Department, Virginia Tech · Computer Science Department, Cornell University
SO3UFormer: Learning Intrinsic Spherical Features for Rotation-Robust Panoramic Dense Prediction
Feb 26, 2026Qinfeng Zhu, Yunxi Jiang, Lei FanDepth Estimation Vision Transformer
University of Liverpool, United Kingdom. · CNRS, France. · Xi’an Jiaotong-Liverpool University, China.
Transformers converge to invariant algorithmic cores
Feb 26, 2026Joshua S. SchiffmanTransformer Interpretability Invariant Representation Learning
New York Genome Center, New York, NY, USA.
Self-Supervised Learning from Structural Invariance
Feb 2, 2026Yipeng Zhang, Hafez Ghaemi, Jungyoon Lee +3Unsupervised Learning Self-Supervised Learning
Mila - Qu´ebec AI Institute · Universit´e de Montr´eal · CHU Sainte-Justine +2
Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units
Feb 2, 2026Zhe Ling, Sicheng Yu, Danyu YangOnline Handwriting Recognition Invariant Representation Learning
College of Mathematics and Statistics, Chongqing University, China
Learning and extrapolating scale-invariant processes
Jan 21, 2026Anaclara Alvez-Canepa, Cyril Furtlehner, François P. LandesInvariant Representation Learning Neural Operators
Laboratoire Interdisciplinaire des Sciences du Numérique (LISN) · Université Paris-Saclay and · INRIA-Saclay
Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification
Jan 17, 2026Xiaomei Yang, Antai Liu, Xizhan Gao +3Invariant Representation Learning Person Re-Identification
Shandong Key Laboratory of Ubiquitous Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China · College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China · Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Via P. Bucci, cubo 41C, 87036, Rende (CS), Italy
Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation
Jan 6, 2026Jarek DudaPrincipal Component Analysis Invariant Representation Learning
Jagiellonian University, Krakow, Poland
Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits
Dec 16, 2025Michael Murray, Tenzin Chan, Kedar Karhadker +1Sample Complexity Neural Network Memorization
Mathematical Sciences, University of Bath · Department of Mathematics, UCLA · Algebraic
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Sep 28, 2025Fanlong Zeng, Wensheng Gan, Kangjie Chen +1Class-Imbalanced Learning Graph Neural Networks
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · School of Cybersecurity, Tianjin University, Tianjin 300354, China · University of Illinois Chicago, Chicago, USA
Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients
Feb 26, 2025Martin Surner, Abdelmajid Khelil, Ludwig BothmannSpurious Correlation Robustness Invariant Representation Learning
Institute for Data and Process Science, Landshut University of Applied Sciences, Landshut, Germany. · Computer Science Department, Landshut University of Applied Sciences, Landshut, Germany. · Department of Statistics, LMU Munich, Munich, Germany. +1
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
Feb 20, 2025Yuling Jiao, Wensen Ma, Defeng Sun +2Visual Representation Learning Distribution Matching
School of Artificial Intelligence, Wuhan University, 430072, Wuhan, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China +2

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Invariant Features in Language Models: Geometric Characterization and Model Attribution

Agnibh Dasgupta, Abdullah Tanvir, Xin Zhong

Department of Computer Science University of Nebraska Omaha, Omaha, NE, USA

Neural Representation Geometry Invariant Representation Learning Representation Geometry in Language Models

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Language models exhibit strong robustness to paraphrasing, suggesting that semantic information may be encoded through stable internal representations, yet the structure and origin of such invariance remain unclear. We propose a local geometric framework in which semantically equivalent inputs occupy structured regions in latent space, with paraphrastic variation along nuisance directions and semantic identity preserved in invariant subspaces. Building on this view, we make three contributions: (1) a geometric characterization of invariant latent features, (2) a contrastive subspace discovery method that separates semantic-changing from semantic-preserving variation, and (3) an application of invariant representations to zero-shot model attribution. Across models and layers, empirical results support these contributions. Invariant structure emerges in specific depth regions, semantic displacement lies largely outside the nuisance subspace, and representation-level interventions indicate a causal role of invariant components in model outputs. Invariant representations also capture model-specific geometric patterns, enabling accurate attribution. These findings suggest that semantic invariance can be viewed as a local geometric property of latent representations, offering a principled perspective on how language models organize meaning.

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