NeurIPS 2025PastGenerative models
NeurIPS 2025 Workshop on Structured Probabilistic Inference & Generative Modeling
SPIGM @ NeurIPS
- Submission deadline
- Aug 31, 2025, 23:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (115)
Fetched from OpenReview (v2) on 2026-06-10.
3-Model Speculative Decoding
A Connection Between Score Matching and Local Intrinsic Dimension
A Multi-Method Interpretability Framework for Probing Cognitive Processing in Deep Neural Networks across Vision and Biomedical Domains
A Probabilistic Approach to Pose Synchronization for Multi-Reference Alignment with applications to wireless communication systems
A Theory of Multi-Agent Generative Flow Networks
Accelerating Diffusion Models in Offline RL via Reward-Aware Consistency Trajectory Distillation
Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing
Ambient Diffusion Omni
An Information-Theoretic Discrete Poisson Diffusion Framework
An Optimal Algorithm for Marginalization in Bayesian Networks
Any-Order Flexible Length Masked Diffusion
Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching
Bayes-PD: Exploring a Sequence to Binding Bayesian Neural Network model trained on Phage Display data
Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling
BioBO: Biology-informed Bayesian Optimization for Perturbation Design
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation
Can We Estimate The Entropy Of Arbitrary Distributions Known Up To A Normalization Constant?
Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design
Continuous-Token Diffusion for Speaker-Referenced TTS in Multimodal LLMs
Contrastive MIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning
CoVAE: Consistency Training of Variational Autoencoders
Cross-Lingual Multimodal Retrieval-Augmented Generation for Open Question Answering in Tamil and Yoruba
DDS-E-Sim: A Transformer-based Probabilistic Generative Framework for Simulating Error-Prone DNA Sequences for DNA Data Storage
DenseMixer: Improving MoE Post-Training with Precise Router Gradient
Diffusion Beats Autoregressive in Data-Constrained Settings
Divergence Minimization Preference Optimization for Diffusion Model Alignment
Effective Diffusion-free Score Matching for Exact Conditional Sampling
Efficient Flow Matching using Latent Variables
Enhancing Diffusion Model Guidance through Calibration and Regularization
Entangled Schrödinger Bridge Matching
Entropy Is Not Enough: Uncertainty Quantification for LLMs fails under Aleatoric Uncertainty
Entropy-Guided Sampling of Flat Modes in Discrete Spaces
Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration
FlowBack-Adjoint: Energy-Guided Conditional Flow-Matching for Protein Side-Chain Generation
Foundations of Top-$k$ Decoding for Language Models
From Entropy Rate to Redundancy: Information Dynamics in Large Language Models
Generalization of Diffusion Models Arises from a Regularized Representation Space
Generative Actor-Critic
GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks
GFlowNets for Learning Better Drug-Drug Interaction Representations
Global Resolution: Optimal Multi-Draft Speculative Sampling via Convex Minimization
GNN-Guided Block Selection in Gibbs MCMC
Graph Random Features for Scalable Gaussian Processes
Hold That Exit: Near Optimal Early-Exit Inference via Recall
IAGA: Identity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
ImmUQBench: A Benchmark on Uncertainty Quantification of Protein Immunogenicity Prediction
Improved Sampling from Masked Diffusion Models with Position Contrastive Guidance
Improving Generation Quality of Long-Tailed Diffusion via Disentangled Latent Representations
Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors
Inception Inference: Nested Probabilistic Reasoning over Story Graphs from Text
Inference and Generating Method for Extremely Sparse Networks
Inference-time Scaling of Diffusion Models through Classical Search
Information-Guided Diffusion Sampling for Dataset Distillation
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions
Is Sequence Information All You Need for Bayesian Optimization of Antibodies?
ISUM: Inverse Problem Solver via Unbalanced Optimal Transport Map
Learning Boltzmann Generators via Constrained Mass Transport
Learning to Iteratively Improve 3D Representation with 2D Generative Models
Learning Velocity Prior-Guided Hamiltonian-Jacobi Flows with Unbalanced Optimal Transport
Leveraging Probabilistic Modeling for Robust End-to-End Autonomous Driving across Domains
MMG: Mutual Information Estimation via the MMSE Gap in Diffusion
moPPIt-v3: Motif-Specific Peptides Generated via Multi-Objective-Guided Discrete Flow Matching
Multi-Objective Nanobody Design via Masked Discrete Diffusion with Simplex Refinement
Multi-scale Autoregressive Models are Laplacian, Discrete, and Latent Diffusion Models In Disguise
Multimodal Bayesian Network for Robust Assessment of Casualties in Autonomous Triage
Myosotis: structured computation for attention like layer
Neural Universal Scene Descriptors
On Fitting Flow Models with Large Sinkhorn Couplings
oPE: Enhanced Transformer with Complex Positional Encoding
Personalized English Amharic Medical Image Caption and Speech Generation for Visually Impaired Patients Using Vision Transformer Fused with LLM
PolUQBench: A Benchmark Study on Uncertainty Quantification of Polymer Property Prediction
Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Probabilistic Image Generation with LLM Priors via Structured Rectified Flow
Probabilistic Soundness Guarantees in LLM Reasoning Chains
Probabilistic Variational Contrastive Learning
Random Projection Flows for Efficient Manifold Density Estimation
Reconsidering Noise for Denoising Diffusion Probabilistic Models
Rethinking Direct Preference Optimization in Diffusion Models
Robust Transfer for Bayesian Optimization with Prior-Data Fitted Networks
Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
ScooBDoob: Schrödinger Bridge with Doob’s h-Transform for Molecular Dynamics
Score-based Idempotent Distillation of Diffusion Models
Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
Selective Underfitting in Diffusion Models
Self-Speculative Decoding in Any-Order and Any-Subset Autoregressive Models
Semantic Probabilistic Control of Language Models
Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
SLayR: Scene Layout Generation with Rectified Flow
Slithering through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
SpecAttn - Speculating Sparse Attention
SpectFlow: Long-term forecasting using flow matching with 89k parameters
State-Space Architectures for Scalable Diffusion-based 3D Molecule Generation
STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability
Steering Pretrained Drafters during Speculative Decoding
Temporal Alignment Guidance: On-manifold Sampling in Diffusion Models
The Unwinnable Arms Race of AI Image Detection
Token-Level Guided Discrete Diffusion for Membrane Protein Design
Tokenized Neural Fields: Structured Representations of Continuous Signals
Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation
Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps
Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
TwinTURBO: Semi-Supervised Fine-Tuning of Foundation Models via Mutual Information Decompositions for Downstream Task and Latent Spaces
Using maximal information auxiliary variables to improve synthetic data generation based on TabPFN foundation models: preliminary results
Value Gradient Guidance for Flow Matching Alignment
Value Matching: Scalable and Gradient-Free Reward-Guided Flow Adaptation
VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillation
Variational Deep Learning via Implicit Regularization
Weighted Conditional Flow Matching
When Do LLMs Improve Bayesian Optimization? A Systematic Comparison Across Molecular and Protein Design
When rule learning breaks: Diffusion Fails to Learn Parity of Many Bits
Where the Score Lives: A Wavelet View of Diffusion
Zero-Variance Gradients for Variational Autoencoders