ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space

arXiv:2604.27443 · cs.LG, cs.AI · Submitted 2026-04-30 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-04-30

Updated: 2026-08-28

Code: https://github.com/gabeguo/abc_diffusion

Project page: https://abc-diffusion.github.io

Terminology

• Diffusion Model — 22× in this paper · explained in 8 episodes
• Diffusion Models — 21× in this paper · explained in 6 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Flow Matching — 11× in this paper · explained in 7 episodes
• Generative Modeling — 8× in this paper · explained in 3 episodes
• Score Function — 8× in this paper · explained in 4 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 5× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Speculative Decoding — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• Robust time series generation via Schr\"odinger Bridge: a comprehensive evaluation
• Mixture of Contexts for Long Video Generation
• Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes
• Variational Inference for SDEs Driven by Fractional Noise
• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Generative modeling for time series via Schr{\"o}dinger bridge
• Trajectory Inference with Smooth Schr\"odinger Bridges
• Longitudinal Flow Matching for Trajectory Modeling
• Adam: A Method for Stochastic Optimization
• Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
• Flow Matching for Generative Modeling
• Let us Build Bridges: Understanding and Extending Diffusion Generative Models
• Latte: Latent Diffusion Transformer for Video Generation
• Fractional Diffusion Bridge Models
• Non-Denoising Forward-Time Diffusions
• Meta Flow Maps enable scalable reward alignment
• Score-Based Generative Modeling through Stochastic Differential Equations
• Momentum Multi-Marginal Schr\"odinger Bridge Matching

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