Unverified50% confidenceFactExact time
Normalizing Trajectory Models (NTM) model each step in the reverse process as an expressive conditional normalizing flow, rather than Gaussian denoising
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/8/2026
First Seen
Valid until: 1/6/2027
Sources
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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.65% similarUnverifiedInference-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.64% similarUnverifiedIn inference networks, nodes represent different LLMs and edges represent conditional model activation (i.e., triggering the next model call only when a certain condition is met).64% similarVerifiedThe core idea of diffusion models originates from non-equilibrium thermodynamics: during the training phase, Gaussian noise is gradually added to clear images, and during the inference phase, noise is progressively removed from random noise in reverse to restore the image.64% similarVerified扩散模型的核心原理分为前向扩散过程（逐步添加高斯噪声）和反向去噪过程（从噪声中还原图像）两个阶段64% similar
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