Compositional Representation Learning

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11 papers in the last four weeks, up 267% on the four weeks before. 0.1% of all new papers.

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CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition
Oct 7, 2026Hyungjoon Juen, Minwoo ShinFourier Neural Operator PDE Operator Learning
Department of Software, Yonsei University (Mirae Campus), Wonju 26493, Republic of Korea
From Global Alignment to Local Grounding: Zero-Shot Chinese Character Recognition with Radical Verification
Oct 7, 2026Yu-Heng Shih, Bing-Chen Wu, Tsz-To Wong +6Zero-Shot Learning Optical Character Recognition
National Yang Ming Chiao Tung University, Taiwan · E.SUN Financial Holding Co., Ltd., Taiwan · National Kaohsiung Normal University, Taiwan
SCION: Scene Composition with Instanced Neural Primitives
Oct 1, 2026William Koch, Amogh Joshi, Cyrus Vachha +23DGS Compression 3D Scene Editing
Princeton University
Learning Expressive and Compositional Motion Representation via Spectral Skills
Sep 29, 2026Feiyang Wu, Chenxiao Gao, Chen Yang +3Robot Skill Learning Humanoid Robot Control
Georgia Institute of Technology
Causal and Interpretable Structures in LLM Compositional Tasks
Sep 28, 2026Gurbir Arora, Toni J. B. Liu, Jiajun Bao +2LLM Interpretability Causal Interventions in Language Models
Cornell University, USA · Goodfire AI, USA
Neural Language Models Learn the Contextual Distributions of Dependency Structures: a statistical learning theory to compositionality
Sep 28, 2026Wang Bojun, Junjie Chen, Holly Jenkins +1Language Modeling Computational Psycholinguistics
Department of Education, University of Oxford, Oxford, United Kingdom · Department of Statistics, University of Oxford, Oxford, United Kingdom
SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition
Sep 28, 2026Zhihan Zeng, Kaihe Wang, José A. López-Salcedo +2Multi-Label Classification Compositional Representation Learning
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China (UESTC), Chengdu, China · Shenzhen Institute for Advanced Study, UESTC, Shenzhen 518110, China · Universitat Aut`onoma de Barcelona, 08193 Barcelona, Spain
A Spectral Theory of Compositional Learning
Sep 27, 2026Hugo RydelComputational Cognitive Modeling Compositional Reasoning
University of Manchester
Order-Invariant Answers, Order-Sensitive Representations in Mathematical Reasoning
Sep 23, 2026Zhixu Silvia TaoMathematical Reasoning Representation Geometry in Language Models
Operations Research and Financial Engineering Princeton University Princeton, NJ 08540
Do Audio Representations Compose Additively?
Sep 23, 2026Chenhao Xue, Zhijin Guo, Joyraj Chakraborty +2Audio Representation Learning Compositional Representation Learning
Department of Engineering Science, University of Oxford, Oxford, UK · University of Essex, Colchester, UK
Canonical locks that encode part-whole hierarchies
Sep 22, 2026Rajat Modi, Yogesh Singh RawatVisual Representation Learning Hierarchical Representation Learning
Department of Computer Science, University of Central Florida, United States.
BindCLIP: One Balanced Coupling For Compositional Vision Language Scoring
Sep 20, 2026Liuyang Song, Yi Zhang, Zhongyi Deng +2Vision-Language Models Vision-Language Grounding
Peking University · Dongguan University of Technology · Sichuan Agricultural University
Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study
Sep 16, 2026Shenjia Ding, David Flynn, Paul HarveyDigital Twins Compositional Representation Learning
School of Computing Science University of Glasgow Glasgow, UK · School of Engineering University of Glasgow Glasgow, UK
CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation
Sep 3, 2026Tingyu Song, Mingxin Li, Yanzhao Zhang +5Multimodal Embedding Cross-Modal Retrieval
CASIA · Alibaba Group · University of Chinese Academy of Sciences +1
A Composition-Aware Pretraining Framework for Geospatial Foundation Models
Aug 31, 2026Aryan Kashyap Naveen, Abhishek Srinivas, Pranav Moothedath +1Remote Sensing Image Understanding Representation Learning
National Institute of Technology, Karnataka · Surathkal, Karnataka, India · Visa Inc +2
TPR-Attention for Combinatorial Generalization
Aug 31, 2026Melisa Civelekoğlu, Isabeau Prémont-SchwarzAttention Mechanisms Compositional Representation Learning
Universit´e Laval · Institut intelligence et donn´ees · Mila – Qu´ebec Artificial Intelligence Institute
Learning visual representations for compositional analysis of artworks and photographs
Aug 6, 2026Fatemeh Behrad, Tinne Tuytelaars, Johan WagemansVisual Representation Learning Compositional Representation Learning
KU Leuven University Belgium
Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning
Jul 24, 2026Haichen Zhou, Yazhe Lyu, Yixiong Zou +2Class-Incremental Learning Incremental Learning
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
Jul 6, 2026Gwang-Ho Na, Ho-Joong Kim, Seong-Whan LeeCross-Modal Alignment Compositional Representation Learning
Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea
Identifying Latent Concepts and Structures for Generalized Category Discovery
Jul 1, 2026Boyang Dai, Chaoqi Chen, Yizhou YuRepresentation Learning Visual Representation Learning
1The University of Hong Kong · 2Shenzhen University
Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval
Jul 1, 2026Jingjing Zhang, Lei Zhang, Zheren Fu +1Representation Learning Compositional Representation Learning
University of Science and Technology of China, Hefei, China
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Jun 22, 2026Sehwan Kim, Yan Sun, Faming LiangNeural Network Generalization Hierarchical Representation Learning
Ewha Womans University, Seoul 03760, Republic of Korea · New Jersey Institute of Technology, Newark, NJ 07102, USA · Purdue University, West Lafayette, IN 47907, USA
Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
Jun 18, 2026Dat H. Do, Rushi Shah, Duc V. Le +1Sparse Neural Networks Compositional Representation Learning
National University of Singapore1 · University of Twente2
RATS! Patches Talk Through Registers: Emergent Parts in Register Attention Transformers
Jun 12, 2026Timing Yang, Predrag Neskovic, Jansen Seheult +4Visual Attention Visual Representation Learning
1Johns Hopkins University · 2Office of Naval Research, Arlington, VA · Department of Laboratory Medicine and Pathology, Mayo Clinic,FLOPs (G)MN, USA
Compositional Generative Modeling from Decentralized Data
Jun 8, 2026Mashrur M. Morshed, Vishnu Naresh BoddetiFlow Matching Decentralized Learning
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning
Jun 4, 2026Moshiur Farazi, Sameera Ramasinghe, Mahbub Ahmed Turza +1Visual Reasoning VLM Reasoning
Data Science and AI, University of Doha for Science and Technology, Qatar · Pluralis Research, Australia · Department of Electrical and Computer Engineering, North South University, Bangladesh
Learning Compositional Latent Structure with Vector Networks
May 27, 2026Niclas Pokel, Benjamin F. GreweRepresentation Learning Recurrent Neural Networks
Institute of Neuroinformatics, UZH / ETH Zurich ETH AI Center Zurich, Switzerland
Learning Sparse Compositional Functions with Norm-Constrained Neural Networks
May 25, 2026Shuo Huang, Lorenzo Fiorito, Lorenzo Rosasco +1Neural Network Generalization Hierarchical Representation Learning
MaLGa, DIMA, Università degli Studi di Genova, Genoa, Italy · CBMM, Massachusetts Institute of Technology, Cambridge, MA, USA
CRISP: Compositional Relations as Invariant Structural Priors for Domain Generalization
May 7, 2026Dat Nguyen, Duc-Duy NguyenRepresentation Learning Visual Representation Learning
Harvard University Basis Research Institute · Hanoi University of Science and Technology
Aitchison Embeddings for Learning Compositional Graph Representations
May 1, 2026Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas +2Neural Representation Geometry Graph Representation Learning
Human Nature Lab, Yale University, New Haven, USA · Universit´e Paris Saclay, Universit´e Paris Cit´e, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France · Yale Institute for Network Science, Yale University, New Haven, USA +2

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CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition

Hyungjoon Juen, Minwoo Shin

Department of Software, Yonsei University (Mirae Campus), Wonju 26493, Republic of Korea

Fourier Neural Operator PDE Operator Learning Fourier Feature Embeddings Compositional Representation Learning

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The Fourier Neural Operator (FNO) learns solution operators of partial differential equations (PDEs) through Fourier-space kernel parameterization, but frequency truncation can limit the learning of high-frequency variations. AM-FNO and SirenFNO generate kernels for all grid modes from spectral coordinates using shared networks, making coordinate encoding and generator design important. Recent work on implicit neural representations (INRs) has proposed constructing frequency interactions through explicit feature composition rather than relying on subsequent MLPs to form them implicitly. Building on this approach, we propose CAFE+FNO, which incorporates Content-Aware Frequency Encoding+ (CAFE+) into Fourier kernel generation. CAFE+ combines Fourier--Chebyshev features through parallel affine branches and a Hadamard product, forming interactions within and across the two feature families. A kernel MLP maps the resulting representation of each normalized spectral coordinate to a complex channel-mixing matrix. Each layer shares its generator across all stored modes, making the number of trainable parameters independent of the number of modes for a fixed architecture. We compare CAFE+FNO with existing FNO variants on five PDE benchmarks and conduct ablation studies on basis configuration, multiplicative composition, and bandwidth learnability. Code and experimental configurations are available at https://github.com/fabsk101/CAFEPlusFNO.git.

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