ICLR 2026 Past Generative models
ICLR 2026 Workshop on Geometry-grounded Representation Learning and Generative Modeling
ICLR 2026 Workshop GRaM
- Submission deadline
- Feb 6, 2026, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (83)
Fetched from OpenReview (v2) on 2026-06-10.
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A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
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A Graph-Theoretical View of Space Folding via the Motzkin–Straus Framework
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A universal compression theory for lottery ticket hypothesis and neural scaling laws
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Adaptive Quasimetric Mapping : Principled Topological Abstraction for Robust Offline Goal-Conditioned Navigation
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Adaptive Symmetry Discovery for Dynamical System Identification
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ADAPTIVEMIXGNN: Local Adaptive Inductive Bias for Heterophilic Node Classification
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Algebraic priors for approximately equivariant networks
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Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
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Balancing Symmetry and Efficiency in Graph Flow Matching
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Beyond Co-occurence: A Study of Early-stage Semantic Geometry in Next-Token Prediction
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Beyond Linearity in Attention Projections: The Case for Nonlinear Queries
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Can Graph Foundation Models Generalize Over Architecture?
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Categorical Trace Loop Networks for Gauge-Randomized Holonomy Regression
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CLERF: Contrastive LEaRning for Full-Range Head Pose Estimation
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Conformal Coordinate Frames for Disentanglement
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Data-Adaptive Relaxed Equivariant Networks for Symmetry Breaking
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DiScoFormer: Plug-In Density and Score Estimation with Transformers
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DO CORESETS, PRUNING, AND QUANTIZATION PRESERVE NEURAL NETWORK REPRESENTATIONS?
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E$(n)$-Equivariant Spherical Decision Surfaces
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Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing
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Embedding Compression via Spherical Coordinates
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Eq-WaLa: Equivariant Augmentation and Regularization for Wavelet Latent Flow Matching
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Flow curvature explains failed SDE drift estimation under sparse sampling
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Fréchet Regression on the Bures-Wasserstein Manifold
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From Leads to Latents: Attention-Driven Masked Autoencoder for ECG Time Series
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Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
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Geometric Inductive Biases for Diffusion-Based Graph Generation
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Geometry-Driven Diverse and Transferable Visual Attacks on Multimodal LLMs
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Geometry-Grounded Flow Matching on Compact Manifolds
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GSVD for Geometry-Grounded Dataset Comparison: An Alignment Angle Is All You Need
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Hyperbolic Curvature as an Inductive Bias for Latent Space Flow Matching
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Hyperbolic Geometry of Reasoning: Probing LLM Hidden States
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Improving LLM Predictions via Inter-Layer Structural Encoders
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InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
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INTRINSIC DIMENSION DYNAMICS IN ACTIVE LEARNING: A GEOMETRIC DIAGNOSTIC OF ACQUISITION BEHAVIOR
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k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS
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Laplacian Flows for Policy Learning from Experience
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Latent Equivariant Operators for Robust Object Recognition: Promises and Challenges
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Learning Compact Representations via Intrinsic Dimension Regularization
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Learning in Transformers under Spectral Constraints
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Lift me up: the impact of liftings on hypergraph neural networks
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LIGHT CONES FOR VISION: SIMPLE CAUSAL PRIORS FOR VISUAL HIERARCHY
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Manifold Generalization Provably Proceeds Memorization in Diffusion Models
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Metric multi-dimensional scaling for longitudinal data embeddings in pharmacometrics
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mHC-lite: You Don't Need 20 Sinkhorn-Knopp Iterations
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Mix Early, Forget Less: Data Mixing During Pretraining Builds Resistance to Forgetting
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Mutual Information and Task-Relevant Latent Dimensionality
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Neurodiversity Meets Colors: Does Position Awareness Destroy Generalization in Brain Graph Learning?
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On Closed-Form Couplings
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On the Expressive Power of Mixed-Curvature Representations in Product Manifolds
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On the Fisher Geometry of Diffusion Models' Latent Space
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On the Geometry of Analogical Reasoning in Latent Space
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On the necessity of learnable sheaf laplacians
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Operator-Consistent Graph Neural Networks for Learning Diffusion Dynamics on Irregular Meshes
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Orthogonal Self-Attention
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Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
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Physics-Aligned Decoding (PAD) for Discrete Protein Structure Representations
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Platonic Transformers: A Solid Choice for Equivariance
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Poisson-Induced Potentials for Contractive representations
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ProCLIP: Product Space Multimodal Contrastive Alignment
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Random but Right: A Geometric Explanation for Efficient LLM Training
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RECYCLE NET: CYCLE-AWARE, FEATURE-FREE GNN FOR COMMUNITY DETECTION
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Riemannian Metric Matching for Scalable Geometric Modelling of Distributions
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Rigid Invariant Sliced Wasserstein via Independent Embeddings
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Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
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Scale Continuity in Graph Learning: Going beyond spectral methods
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Semantic-Anchored, Class Variance-Optimized Clustering for Robust Semi-Supervised Few-Shot Learning
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Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
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Solvaformer: Minimizing Geometric Redundancy for Scalable Solubility Prediction
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Sparse Concept Anchoring for Interpretable and Controllable Neural Representations
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Spatio-Spectral Sequence Processing
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Symmetry, Gauss-Newton, and Whitening in Neural Network Optimization
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Tensor-SAE: Structured Sparse Autoencoders for Interpretable and Efficient Image Representations
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The Affine Divergence: Aligning Activation Updates Beyond Normalisation
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The Geometrical and Topological Signature of Transformers
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The Geometry of Spectral Gradient Descent: Layerwise Criteria for SignSGD vs SpecSGD
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Topological Invariance and Breakdown in Learning
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TopoPointPWC: Manifold Topology-Aware Point Cloud Registration via Persistent Homology
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Towards a Geometric Theory of Fairness: Detecting Mode Collapse on the Grassmannian Manifold
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Towards Scalable Persistence-Based Topological Optimization
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Towards Text-Line Segmentation of Historical Documents Using Graph Neural Networks
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TPR-Attention for Combinatorial Generalization
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Weak-SIGReg: Covariance Regularization for Stable Deep Learning