P4 · Generative priors · since 2026 · ascendant
Autoregressive and flow-based synthesis
The generative-prior idea carried to visual autoregressive (next-scale) models and flow-matching generators.
The bet every member shares
The structure of other generative families — coarse-to-fine token scales, straight transport paths — offers control points for representativeness that diffusion sampling does not.
How the family developed
The papers that moved the family, in the role each one played.
Origin
Path-guided flow matching · arXiv 2026
Presented as the first flow-matching framework for generative distillation: class-conditional transport in a frozen VAE's latent space, with ODE-consistent guidance that lands trajectories on assigned prototypes in few steps.
HIERAMP · CVPR 2026
Uses a visual autoregressive model's coarse-to-fine scales, injecting class tokens at each scale to amplify discriminative structure rather than optimizing global proximity.
What it gets right
- Coarse-to-fine or path-level control gives a natural place to inject dataset-level guidance.
Where it is weak
- Very recent; few papers, and evidence is concentrated on ImageNet-style benchmarks.
Applied to
Papers per year
Competing answers
All papers in this family 3
Papers not already discussed above, ordered by tier, then newest first.Open in the explorer →
ProtoVAR — ProtoVAR: Efficient Dataset Distillation via Prototype-Guided Visual Autoregressive Modeling
Mingyu Wang, Wei Jiang · ICML 2026paper ↗
Replaces diffusion sampling with a visual autoregressive (VAR) model guided by multi-scale class prototypes, adding a fast filtering stage that keeps only the most informative generated samples; reported to run up to 70x faster than diffusion-based generative distillation while remaining competitive on ImageNet-scale benchmarks.