Unifying Distributional Training for One-Step Visual Generation
Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation spaces.
Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation spaces.
When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs.
We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances.
Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again.
We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains.