Metraipolis › archive › dc_441cfeb06daf

Second Order Drifting Models — arXiv:2608.07924

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.07924
License (as archived): CC0-1.0
Archived: 2026-10-19T09:08:09.499Z
SHA-256 of the archived text: 7af2609f5738e2cb86b303d8a6660e8349b8d808d3ae34cb1e84ac428e5563eb
Stable URL of this page: https://metraipolis.com/documento/dc_441cfeb06daf

How to cite

Second Order Drifting Models — arXiv:2608.07924 — arXiv (en), https://arxiv.org/abs/2608.07924 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_441cfeb06daf (Metraipolis archive dc_441cfeb06daf, sha256 7af2609f5738e2cb86b303d8a6660e8349b8d808d3ae34cb1e84ac428e5563eb)

BibTeX

@misc{dc441cfeb06daf,
  title = {Second Order Drifting Models — arXiv:2608.07924},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.07924},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_441cfeb06daf; sha256 7af2609f5738e2cb86b303d8a6660e8349b8d808d3ae34cb1e84ac428e5563eb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.07924. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Drifting models are a recent class of one-step generative models that evolve the model distribution during training using a predefined sample-based drift field. Although they avoid iterative inference, their kernel-based drift fields induce frequency-dependent training dynamics: In the linearized regime, each Fourier mode of the density residual decays at a rate determined by the kernel spectrum, leading to slow recovery of fine-scale structure. We propose Second-Order Drifting Models, which lift drifting dynamics into phase space by augmenting generated samples with artificial velocity variables. We show that the resulting density perturbations obey accelerated second-order dynamics in Fourier space, connecting drifting models to the celebrated Nesterov acceleration from optimization theory. This provides a principled mechanism for mitigating the spectral stiffness of first-order drifting while preserving one-step inference. We derive a practical semi-implicit training algorithm and evaluate it on synthetic distribution matching, sequential data generation, and robotic control. Across these settings, the second-order drifting model improves convergence behavior and achieves competitive or superior performance over first-order drifting baselines.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-08-11 in math.NA).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Numerical Analysis (math.NA).
Pagina del lavoro: https://arxiv.org/abs/2608.07924 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:05:02.993Z dalla pagina https://arxiv.org/catchup/math.NA/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).