ICML 2026PastGenerative models
ICML 2026 Workshop on Structured Probabilistic Inference & Generative Modeling
SPIGM @ ICML
- Submission deadline
- May 9, 2026, 12:00 UTCOpenReview-synced 2026-05-09 12:00 UTC (as of 2026-06-23) — extensions on OpenReview are applied automatically; verify on the website.
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (208)
Fetched from OpenReview (v2) on 2026-06-10.
$\psi$DAG: Projected Stochastic Approximation Iteration for Linear DAG Structure Learning
A Born Machine Approach to Controllable Text Generation with Language Models
A Generative Model for Extremely Sparse Edge-Exchangeable Networks
A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
A Structural View of Query Misspecification in Causal Foundation Models
A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
A Unified View of Score-Based and Drifting Models
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Aligning Few-Step Generative Model via Amortizing Sample-Based Variational Inference
Alignment-Dependent Inference in Small Language Models via Budgeted Marginalization over Contextual Priors
AMIGO: Adapters Meet Information Geometry
Amortised Inference through One-Step Implicit Sampling
Analytic interdomain memory for efficient online HiPPO-SVGP
Anchoring Aleatoric Uncertainty: A Four-Term Decomposition of Predictive Risk at the Bayes-Optimal Predictor
Applying Splat Regression Models to Particle Density Control in Radiance Fields
Benchmarks as Random Variables—Modeling Overdispersion in LLM Evaluation
BIRDGen: Multimodal Conditional Inference of Latent Unbiased Species Distributions
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
Branching Diffusion for Point Processes in Time and Space
Breaking the Factorization Barrier in Diffusion Language Models
Bridging the Gap Between AI Predictions and Chemical Conventions: Template-Guided Reranking for Accurate Reagent Set Suggestion
Calibrating Promptable Concept Segmentation via Paraphrase Consistency
Categorical Drifting Models
CIRCUS: Circuit Consensus under Uncertainty via Stability Ensembles
Compositional Energy-Based Inference-Time Scaling for Multi-Scale Microstructure Generation
Compositional Flow Matching with Factored Velocity Fields
Conditional Inference Mismatch in Structured Diffusion Language Models
Conditional Random Fields for Structured Representation Learning from Pretrained Features
Conditional Unbalanced Optimal Transport Maps: An Outlier-Robust Framework for Conditional Generative Modeling
Context Over Content: Exposing Evaluation Faking in Automated Judges
Context-Aware Neural SDEs for Robust Irregular Time-Series Classification
Contour Monte Carlo: Sampling via Energy Level Sets
Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
Decision-Aware Training for Sample-Based Generative Models
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
Deep Heteroskedastic Regression: Post-Hoc Variance Estimation from Latent Representations
DELTA-TTS: Adapting Autoregressive Model into a Diffusion Language Model for Text-to-Speech
Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations
Diffusion Accelerants: Towards Augmenting Molecular Dynamics with Learned Measure Transport
Diffusion Gaussian Processes
Direct Flow Neural Processes: Efficient Sampling via Flow Step Amortization
Discrete Langevin-Inspired Posterior Sampling
DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models
DODO: Discrete OCR Diffusion Models
DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling
DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking
DVD: Discrete Voxel Diffusion for 3D Generation and Editing
Edge-aware FlexAttention Network for Efficient Graph Generation
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
Efficient One-to-many Domain Translation via Diffusive Entropic Optimal Transport
Electrostatic Models for Score Matching
End-to-End Context Compression at Scale
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Evolutionary Curriculum Learning for Biological Sequence Modeling
Exact Posterior Score Estimation for Solving Linear Inverse Problems
Expanding Flow Maps
Extracting Local Manifold Geometry from Pretrained Diffusion Models in One Inverse Step
Factored Score Matching on Graphical Models: Exact Computation on Trees and Convergent Approximation on Loopy Graphs
FairOpt-PFN: Amortized Counterfactual Fairness with Optimal Fair Targets
Fast-dLLM++: Fr\'{e}chet Profile Decoding for Faster Diffusion LLM Inference
Faster Inference for Conditional Masked Diffusion Language Models by Knowledge Distillation of Guidance and Trajectory
Federated Learning with Energy-Based Structured Probabilistic Inference
Federated Sampling of Molecular Conformers via Compositional Flows
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
Finetuning Generative Models to Match Feature Distributions
Fisher-constrained flow matching for transferable free energy estimation
Fixed-Point Distillation of Flow Matching Models
Fixed-Point Masked Generative Modeling
Flash-SD-KDE: Accelerating SD-KDE with Tensor Cores
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
Flow Matching for Reaction Pathway Generation
Flow Matching on General Manifolds via Pulling Back Geodesic Convex Latent Manifolds
FM-DeepRV: Deep Learning for Bayesian Inference with Flow Matching
Forecasting Motion in the Wild
Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance
From Fisher--Rao Simplex Flows to Canonical Jump Generators: A $\Gamma$-Convergence Theory of Discrete Flow Matching
Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D
GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
Gaussian Particle Flows for Unsupervised Topology Optimization
Gene-Embedding Perturbation Operators for Zero-Shot and Transferable Prediction of Transcriptional Responses
Generalised Latent Slice Sampling
Generative Modeling via Kernelized Stochastic Interpolants
GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
Holistic Latent Diffusion Acceleration: Unifying Spatial, Temporal, and Architectural Efficiency
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Foundation Models
How to Train Your Latent Diffusion Language Model Jointly With the Latent Space
Hyperbolic Latent Geometry for Tree-Structured Prototype Networks: A Local-vs-Global Trade-off
Implicit Neural Representations of Individual Behavior
Improving Conformal Prediction Sets Through Semantic Neighborhood Diffusion
Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
Inter-Trajectory Importance Sampling Improves Diffusion Samplers
Internal Data Repetition Destroys Language Models
Inverse problems with diffusion models: MAP estimation via mode-seeking loss
Inverse-Confidence Sampling for Continuous Diffusion Language Models
Inverting Foundation Models of Brain Function with Simulation-Based Inference
Irregularities of Latent Space Geometry in Diffusion Models
Isokinetic Flow Matching for Pathwise Straightening
Just Add More Capacitors: Eliminating Flux Leakage in Electrostatic Field Matching
Kernel-Gradient Drifting Models
LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
Language Models Need Sleep
Latent-Augmented Discrete Diffusion Models
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
Learning Adapter Rank via Symmetry Breaking
Learning Manifold Data with Flow Matching
Learning path splines via Acceleration Matching
Learning Shortest Paths with Generative Flow Networks
Learning to Shift Numeric Predictive Densities for Uncertainty-Aware LLM Agents
Leveraging Generative Mode-Seeking for Precision Matrix Estimation
Limit Order Book Forecasting with Conditional Diffusion Models
MCD-RRG: Time-Varying Multimodal Fusion and Residual Retrieval Guidance for Conditional Diffusion
Measuring and Reducing Train--Inference Mismatch in Discrete Diffusion Language Models
Midpoint Generative Models
MIRROR: Multisensory Implicit Rejection-sampled RObotic policy
MIST: Mutual Information Estimation via Supervised Training
Model-Free Assessment of Simulator Fidelity via Quantile Curves
Multilingual Synthetic Scanpaths: Cross-Language Generalization for Gaze Generation
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Noise Scheduling as Information-Guided Allocation in Diffusion Training
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
Normalizing Trajectory Models
On Calibration of Modern Language Models
On the Difficulty of Feature Unlearning in Tabular Diffusion Models
ORBIT: Counterfactual Proposal Inference for Prompt-Free 3D Brain Tumor Segmentation
Order-Agnostic Decoding for Sample-Efficient RNA Inverse Folding
OrthoBO: Orthogonal Bayesian Hyperparameter Optimization
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
Path-independent Flow Matching for Multi-parameter Generative Dynamics
Perfect Recall, Parallel Efficiency: Interleaved DeepSeek Sparse Attention for Million-Token-Context Decoding
Pi-E-Flow: Uncertainty-Guided Flow Distillation for Autoregressive Video Generation
Plan, Don’t Pose: Long Composite Motion Generation with Text-Aligned BFM
Position: Benchmark Method-Comparisons Are Posterior Identifiability Problems
Position: Multi-Agent LLM Simulation as Approximate Posterior Inference Demands a Probabilistic Calibration Standard
Prior-Informed Flow Matching for Graph Reconstruction
PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM
Probabilistic Chain-of-Thought: Sequential Bayesian Inference over Latent Reasoning Correctness
Probabilistic Sequence Generation Guided by Intensity-Duration Extreme Profiles
Provably Stable Neural Dynamics via Koopman Operator Certificates
Proximal Policy Optimization for Amortized Discrete Sampling
Random-Projection Tree Stein Variational Gradient Descent
Rao-Blackwellized Score Matching on Manifolds
RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Readout Times Are Not Solver Nodes: A Two-Mesh API for Generative ODE Surrogates
ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE
Reconsidering Positional Supervision in Masked Diffusion Language Model Training
Registers Matter for Pixel-space Diffusion Transformers
Residual-Space Evolutionary Optimization via Flow-based Generative Models
Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Reward-Aligning Few-Step Flow Models with Integrated Regularizers
Reweighted ALPS: Non-Asymptotic Guarantees for Multimodal Sampling with Warm Starts
SALSA: State Augmentation via Learned Selective Attention
SASC: Soft-Averaged Self-Consistency to Improve Chain-of-Thought Reasoning in Instruct-LLMs
Savitar: Curve-Aware Interaction-Structured Kernels for Low-Budget Bayesian Optimization in Rare-Winner Combinatorial Spaces
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
Scalable Deep Basis Kernel Gaussian Processes
Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes
Scalable Inference-Time Steering in Molecular Design with Multimodal Meta Flow Maps
Scaling with Recursion in Masked Discrete Diffusion Models
Self-Supervised Variational Priors for Robust Bayesian Inference
Signal from Structure: Exploiting Submodular Upper Bounds in Generative Flow Networks
Single-Step Initialization for Exploratory Parallel Rollouts in Diffusion LLMs
Size- and Dispersion-Corrected Two-Level Softmax Sampling
Solving Integer Linear Programming with Parallel Tempering
SpatialNP: Gridded Transformer Neural Processes for Probabilistic Spatial Proteomics in Multiplexed Tissue Imaging
Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
STARS: Synchronous Token Alignment for Robust Supervision in Large Language Models
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
Strong Stochastic Flow Maps
Structural Support Certificates for Graph-Query Inference in Mechanism Posteriors
Structured Coupling for Flow Matching
Structured Inference with Large Language Gibbs
Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs
Synthesizability-Aware Materials Generation with Target Properties via Reinforcement Learning
Systematic Study of Grid Adaptation Strategies in Kolmogorov–Arnold Networks
TAPS: Task Aware Proposal Distributions for Speculative Sampling
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion
The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
TILT: Test-Time Reward Alignment via Distribution Tilting for Compositional Generation
Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
Time-Correlated Video Bridge Matching
Topological Control of Optimization Dynamics on Evolving Manifolds
Towards Closing the Autoregressive Gap in Language Modeling via Entropy-Gated Continuous Bitstream Diffusion
TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
Uncertainty Estimation for Molecular Diffusion Models
Uncertainty Quantification for LLM Agents via Semantic Abstraction Trajectories
Understanding and Accelerating the Training of Masked Diffusion Language Models
Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
Unlocking the Duality between Flow and Field Matching
Variance Reduction for Expectations with Diffusion Teachers
Variance-Tilted Diffusion Models for Diverse Sampling
WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
When are likely answers right? On Sequence Probability and Correctness in LLMs
When Does a Low-Rank Bayesian Neural Network Certify Its Deterministic Center?
When Inference-Time Reward Steering Hacks the Reward
Your Autoregressive Model Already Reveals the Causal Graph
Your GFlowNet Secretly Learns an Optimal Transport Plan