Error-Corrected Inference-Time Scaling for Imperfect Diffusion Models

arXiv:2610.01933 · stat.ML, cs.LG · Submitted 2026-10-01 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Diffusion Model — 48× in this paper · explained in 8 episodes
• Diffusion Models — 33× in this paper · explained in 6 episodes
• Trace — 10× in this paper · explained in 2 episodes
• Degeneracy — 7× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Failure Modes — 5× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Sequential Monte Carlo — 4× in this paper
• Variance-Controlling Guidance — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Gaussian Mixture Model (GMM) — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Langevin Dynamics — 2× in this paper · explained in 2 episodes
• Score Function — 2× in this paper · explained in 4 episodes
• Sequential Monte Carlo (SMC) — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Inference-Time Steering — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Discrete Feynman-Kac Correctors
• Metropolis-Adjusted Diffusion Models
• Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
• DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
• FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
• Test-time scaling of diffusions with flow maps
• Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
• Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
• Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences

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