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TOPIC · ENTITY #18578

classifier-free guidance (CFG)

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RESEARCH · 1 SOURCE · arXiv cs.AI

Temporal Specification Guidance (TSG) for controllable text-to-video generation

The paper introduces Temporal Specification Guidance (TSG), a training-free inference-time framework that encodes temporal requirements as Metric Temporal Logic specifications and turns them into differentiable satisfaction scores over video-text attention traces; gradients of these scores produce attention biases that steer denoising and augment classifier-free guidance. Evaluated on a 220-prompt benchmark spanning 11 temporal constraint categories, this instantiation improves specification-satisfaction accuracy by 9.5–20 percentage points across five text-to-video models, without external concept classifiers or backpropagation through the video transformer.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Correction Training for Diffusion Models (arXiv:2610.14136v2)

The paper introduces a learned "corrector" that is trained on the base denoiser's own predictions to close the training–inference gap caused by exposure bias in iterative diffusion sampling. At each denoising step the corrector iteratively refines the denoiser's clean-sample prediction; evaluated on DiT, SiT, and JiT this approach substantially improves FID for unguided and unconditional generation, is competitive with classifier-free guidance (CFG), can be combined with CFG to improve diversity, and its inference cost can be removed by distilling refined predictions back into the base model.

7.0