• 3. From Fisher--Rao Simplex Flows to Canonical Jump Generators: A $\Gamma$-Convergence Theory of Discrete Flow Matching
• 6. A Generative Model for Extremely Sparse Edge-Exchangeable Networks
• 7. Flow Matching for Reaction Pathway Generation
• 8. Neuro-Symbolic ODE Discovery with Latent Grammar Flow
• 9. DVD: Discrete Voxel Diffusion for 3D Generation and Editing
• 10. The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
• 11. $\psi$DAG: Projected Stochastic Approximation Iteration for Linear DAG Structure Learning
• 12. Systematic Study of Grid Adaptation Strategies in Kolmogorov–Arnold Networks
• 13. Topological Control of Optimization Dynamics on Evolving Manifolds
• 16. STARS: Synchronous Token Alignment for Robust Supervision in Large Language Models
• 17. Context Over Content: Exposing Evaluation Faking in Automated Judges
• 18. Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations
• 20. CFG-OEC: Classifier Free Guidance with Orthogonal Error Correction
• 21. Probabilistic Chain-of-Thought: Sequential Bayesian Inference over Latent Reasoning Correctness
• 22. A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
• 23. Branching Diffusion for Point Processes in Time and Space
• 24. Inverting Foundation Models of Brain Function with Simulation-Based Inference
• 25. Rao-Blackwellized Score Matching on Manifolds
• 26. Direct Flow Neural Processes: Efficient Sampling via Flow Step Amortization
• 27. TAPS: Task Aware Proposal Distributions for Speculative Sampling
• 28. U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
• 30. DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models
• 31. Signal from Structure: Exploiting Submodular Upper Bounds in Generative Flow Networks
• 32. Learning Shortest Paths with Generative Flow Networks
• 33. GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
• 35. Finetuning Generative Models to Match Feature Distributions
• 37. MCD-RRG: Time-Varying Multimodal Fusion and Residual Retrieval Guidance for Conditional Diffusion
• 40. Model-Free Assessment of Simulator Fidelity via Quantile Curves
• 41. PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
• 42. When Does a Low-Rank Bayesian Neural Network Certify Its Deterministic Center?
• 43. Recursive Scaling in Masked Diffusion Models
• 44. Diffusion Gaussian Processes
• 46. Aligning Few-Step Generative Model via Amortizing Sample-Based Variational Inference
• 47. Random-Projection Tree Stein Variational Gradient Descent
• 48. Conditional Unbalanced Optimal Transport Maps: An Outlier-Robust Framework for Conditional Generative Modeling
• 50. Multilingual Synthetic Scanpaths: Cross-Language Generalization for Gaze Generation
• 52. Variance-Tilted Diffusion Models for Diverse Sampling
• 53. Perfect Recall, Parallel Efficiency: Interleaved DeepSeek Sparse Attention for Million-Token-Context Decoding
• 54. Proximal Policy Optimization for Amortized Discrete Sampling
• 55. Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
• 56. Alignment-Dependent Inference in Small Language Models via Budgeted Marginalization over Contextual Priors
• 57. ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
• 59. Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
• 60. Benchmarks as Random Variables—Modeling Overdispersion in LLM Evaluation
• 61. Wasserstein Gradient Flows and Forward-Only Diffusion Are Not Enough for Multimodal Sampling
• 62. How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Foundation Models
• 63. Forecasting Motion in the Wild
• 64. Path-independent Flow Matching for Multi-parameter Generative Dynamics
• 65. Diffusion Accelerants: Towards Augmenting Molecular Dynamics with Learned Measure Transport
• 66. Calibrating Promptable Concept Segmentation via Paraphrase Consistency
• 67. Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
• 68. Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance
• 69. Size- and Dispersion-Corrected Two-Level Softmax Sampling
• 70. GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
• 71. Latent-Augmented Discrete Diffusion Models
• 72. Your Autoregressive Model Already Reveals the Causal Graph
• 73. On the Difficulty of Feature Unlearning in Tabular Diffusion Models
• 74. Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
• 75. FairOpt-PFN: Amortized Counterfactual Fairness with Optimal Fair Targets
• 76. Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs
• 77. WarmPrior: Straightening Flow-Matching Policies with Temporal Priors
• 78. Generalised Latent Slice Sampling
• 79. Solving Integer Linear Programming with Parallel Tempering
• 82. Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
• 83. Learning Adapter Rank via Symmetry Breaking
• 85. Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
• 86. Fixed-Point Distillation of Flow Matching Models
• 87. How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
• 88. Reconsidering Positional Supervision in Masked Diffusion Language Model Training
• 89. Reweighted ALPS: Non-Asymptotic Guarantees for Multimodal Sampling with Warm Starts
• 90. End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
• 91. On Calibration of Modern Language Models
• 92. Analytic interdomain memory for efficient online HiPPO-SVGP
• 93. Structured Coupling for Flow Matching
• 94. Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
• 95. End-to-End Context Compression at Scale
• 96. Prior-Informed Flow Matching for Graph Reconstruction
• 97. Learning Manifold Data with Flow Matching
• 98. Isokinetic Flow Matching for Pathwise Straightening
• 100. DODO: Discrete OCR Diffusion Models
• 101. Variance Reduction for Expectations with Diffusion Teachers
• 102. Limit Order Book Forecasting with Conditional Diffusion Models
• 103. Re-evaluating Confidence Remasking in Masked Diffusion Language Models
• 104. Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
• 105. Scalable Deep Basis Kernel Gaussian Processes
• 108. Anchoring Aleatoric Uncertainty: A Four-Term Decomposition of Predictive Risk at the Bayes-Optimal Predictor
• 109. Language Models Need Sleep
• 111. Pi-E-Flow: Uncertainty-Guided Flow Distillation for Autoregressive Video Generation
• 112. Savitar: Curve-Aware Interaction-Structured Kernels for Low-Budget Bayesian Optimization in Rare-Winner Combinatorial Spaces
• 113. MIRROR: Multisensory Implicit Rejection-sampled RObotic policy
• 115. DELTA-TTS: Adapting Autoregressive Model into a Diffusion Language Model for Text-to-Speech
• 116. Fixed-Point Masked Generative Modeling
• 117. Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
• 118. Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
• 119. OrthoBO: Orthogonal Bayesian Hyperparameter Optimization
• 120. Faster Inference for Conditional Masked Diffusion Language Models by Knowledge Distillation of Guidance and Trajectory
• 122. Midpoint Generative Models
• 123. A Structural View of Query Misspecification in Causal Foundation Models
• 124. Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes
• 125. Deep Heteroskedastic Regression: Post-Hoc Variance Estimation from Latent Representations
• 126. A Born Machine Approach to Controllable Text Generation with Language Models
• 127. FM-DeepRV: Deep Learning for Bayesian Inference with Flow Matching
• 128. Strong Stochastic Flow Maps
• 130. PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM
• 131. Unlocking the Duality between Flow and Field Matching
• 133. AMIGO: Adapters Meet Information Geometry
• 134. Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
• 135. CIRCUS: Circuit Consensus under Uncertainty via Stability Ensembles
• 136. Applying Splat Regression Models to Particle Density Control in Radiance Fields
• 137. Synthesizability-Aware Materials Generation with Target Properties via Reinforcement Learning
• 138. Inverse problems with diffusion models: MAP estimation via mode-seeking loss
• 139. Your GFlowNet Secretly Learns an Optimal Transport Plan
• 140. Improving Conformal Prediction Sets Through Semantic Neighborhood Diffusion
• 141. Residual-Space Evolutionary Optimization via Flow-based Generative Models
• 142. Registers Matter for Pixel-space Diffusion Transformers
• 143. A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
• 144. When are likely answers right? On Sequence Probability and Correctness in LLMs
• 146. Structured Inference with Large Language Gibbs
• 147. Amortised Inference through One-Step Implicit Sampling
• 148. Uncertainty Quantification for LLM Agents via Semantic Abstraction Trajectories
• 149. Exact Posterior Score Estimation for Solving Linear Inverse Problems
• 150. ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE
• 151. Towards Closing the Autoregressive Gap in Language Modeling via Entropy-Gated Continuous Bitstream Diffusion
• 152. Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
• 157. Efficient One-to-many Domain Translation via Diffusive Entropic Optimal Transport
• 158. RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
• 159. Contour Monte Carlo: Sampling via Energy Level Sets
• 161. Just Add More Capacitors: Eliminating Flux Leakage in Electrostatic Field Matching
• 162. Decision-Aware Training for Sample-Based Generative Models
• 163. Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
• 164. Hyperbolic Latent Geometry for Tree-Structured Prototype Networks: A Local-vs-Global Trade-off
• 165. Breaking the Factorization Barrier in Diffusion Language Models
• 166. Structural Support Certificates for Graph-Query Inference in Mechanism Posteriors
• 167. Electrostatic Models for Score Matching
• 168. Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
• 169. Flow Matching on General Manifolds via Pulling Back Geodesic Convex Latent Manifolds
• 170. DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking
• 171. Noise Scheduling as Information-Guided Allocation in Diffusion Training
• 172. SASC: Soft-Averaged Self-Consistency to Improve Chain-of-Thought Reasoning in Instruct-LLMs
• 173. Conditional Random Fields for Structured Representation Learning from Pretrained Features
• 174. Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
• 175. Expanding Flow Maps
• 177. Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
• 178. MIST: Mutual Information Estimation via Supervised Training
• 179. Provably Stable Neural Dynamics via Koopman Operator Certificates
• 180. Discrete Langevin-Inspired Posterior Sampling
• 181. Efficient Edge-aware Attention Network for Graph Generation
• 182. Scalable Inference-Time Steering in Molecular Design with Multimodal Meta Flow Maps
• 183. Plan, Don’t Pose: Long Composite Motion Generation with Text-Aligned BFM
• 184. Implicit Neural Representations of Individual Behavior
• 185. LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
• 186. Normalizing Trajectory Models
• 187. Time-Correlated Video Bridge Matching
• 189. Measuring and Reducing Train--Inference Mismatch in Discrete Diffusion Language Models
• 190. Compositional Flow Matching with Factored Velocity Fields
• 191. Holistic Latent Diffusion Acceleration: Unifying Spatial, Temporal, and Architectural Efficiency
• 192. Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
• 194. Learned Relay Representations for Forward-Thinking Discrete Diffusion Models
• 198. Factored Score Matching on Graphical Models: Exact Computation on Trees and Convergent Approximation on Loopy Graphs
• 199. Bridging the Gap Between AI Predictions and Chemical Conventions: Template-Guided Reranking for Accurate Reagent Set Suggestion
• 200. The Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
• 201. STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
• 202. SpatialNP: Gridded Transformer Neural Processes for Probabilistic Spatial Proteomics in Multiplexed Tissue Imaging
• 203. Flash-SD-KDE: Accelerating SD-KDE with Tensor Cores
• 206. When Inference-Time Reward Steering Hacks the Reward
• 209. Gene-Embedding Perturbation Operators for Zero-Shot and Transferable Prediction of Transcriptional Responses
• 211. ORBIT: Counterfactual Proposal Inference for Prompt-Free 3D Brain Tumor Segmentation
• 212. Federated Learning with Energy-Based Structured Probabilistic Inference
• 214. Evolutionary Curriculum Learning for Biological Sequence Modeling
• 215. Understanding and Accelerating the Training of Masked Diffusion Language Models
• 216. Fast-dLLM++: Fr\'{e}chet Profile Decoding for Faster Diffusion LLM Inference
• 217. Uncertainty Estimation for Molecular Diffusion Models
• 218. Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D
• 219. Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
• 222. Internal Data Repetition Destroys Language Models
• 223. Kernel-Gradient Drifting Models
• 224. Probabilistic Sequence Generation Guided by Intensity-Duration Extreme Profiles
• 225. Scale Dependent Data Duplication
• 226. Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
• 227. Inter-Trajectory Importance Sampling Improves Diffusion Samplers
• 229. DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling
• 231. SALSA: State Augmentation via Learned Selective Attention
• 232. TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
• 234. Categorical Drifting Models
• 235. TILT: Test-Time Reward Alignment via Distribution Tilting for Compositional Generation
• 238. Context-Aware Neural SDEs for Robust Irregular Time-Series Classification
• 239. Learning path splines via Acceleration Matching
• 240. Tensor-Train Joint Modeling for Few-Step Discrete Diffusion
• 242. Compositional Energy-Based Inference-Time Scaling for Multi-Scale Microstructure Generation
• 243. Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion
• 244. Order-Agnostic Decoding for Sample-Efficient RNA Inverse Folding
• 245. Inverse-Confidence Sampling for Continuous Diffusion Language Models
• 246. Learning to Shift Numeric Predictive Densities for Uncertainty-Aware LLM Agents
• 248. Irregularities of Latent Space Geometry in Diffusion Models
• 251. Extracting Local Manifold Geometry from Pretrained Diffusion Models in One Inverse Step
• 254. Federated Sampling of Molecular Conformers via Compositional Flows
• 256. Position: Multi-Agent LLM Simulation as Approximate Posterior Inference Demands a Probabilistic Calibration Standard
• 257. Position: Benchmark Method-Comparisons Are Posterior Identifiability Problems
• 258. Self-Supervised Variational Priors for Robust Bayesian Inference
• 259. How to Train Your Latent Diffusion Language Model Jointly With the Latent Space
• 260. Gaussian Particle Flows for Unsupervised Topology Optimization
• 261. Leveraging Generative Mode-Seeking for Precision Matrix Estimation
• 262. Single-Step Initialization for Exploratory Parallel Rollouts in Diffusion LLMs
• 263. Readout Times Are Not Solver Nodes: A Two-Mesh API for Generative ODE Surrogates
• 264. BIRDGen: Multimodal Conditional Inference of Latent Unbiased Species Distributions
• 265. Reward-Aligning Few-Step Flow Models with Integrated Regularizers
• 267. Conditional Inference Mismatch in Structured Diffusion Language Models
• 268. Fisher-constrained flow matching for transferable free energy estimation
• 269. A Unified View of Score-Based and Drifting Models
• 270. Generative Modeling via Kernelized Stochastic Interpolants