Unverified50% confidenceFactExact time
Inference-time guidance steers sampling trajectories toward regions consistent with observations by adding the gradient of the observation likelihood at each step of reverse diffusion or flow, without requiring model retraining.
1
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50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
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Related Claims
UnverifiedThe method trains a single conditional latent diffusion model that can advance a dynamical system forward in time or trace it backward in time based on a direction flag.76% similarUnverifiedCompared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.73% similarUnverifiedDuring the inference phase, only forward propagation needs to be executed; gradient computation and backpropagation are not required.73% similarUnverifiedScheduled Sampling is a compromise approach to mitigating exposure bias, gradually introducing the model's own predictions as input during the later stages of training.72% similarUnverifiedRolling preview refers to a model's continuous iteration by frequently pushing checkpoints before official release, where capabilities between adjacent versions may exhibit inconsistent fluctuations rather than monotonic improvement.72% similar
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