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Asynchronous Flow Map Language Models — arXiv:2610.42018

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.42018
License (as archived): CC0-1.0
Archived: 2026-11-02T07:03:53.838Z
SHA-256 of the archived text: 69f0b8e4a00c1ddc76be878deb531b57a3db1d3765a41302d2a50eaffada32cc
Stable URL of this page: https://metraipolis.com/documento/dc_fd35ecafaf89

How to cite

Asynchronous Flow Map Language Models — arXiv:2610.42018 — arXiv (en), https://arxiv.org/abs/2610.42018 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fd35ecafaf89 (Metraipolis archive dc_fd35ecafaf89, sha256 69f0b8e4a00c1ddc76be878deb531b57a3db1d3765a41302d2a50eaffada32cc)

BibTeX

@misc{dcfd35ecafaf89,
  title = {Asynchronous Flow Map Language Models — arXiv:2610.42018},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.42018},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fd35ecafaf89; sha256 69f0b8e4a00c1ddc76be878deb531b57a3db1d3765a41302d2a50eaffada32cc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.42018. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The order in which tokens are predicted in language modeling has a profound impact on the quality of generated sequences. Autoregressive and masked diffusion models iteratively fill in tokens and can exploit such orderings across generation steps, but how to leverage an ordering for one-step parallel language modeling is unclear. We introduce asynchronous flow map language models (aFMLMs), which incorporate prediction order into one-step modeling by learning the flow maps of asynchronous flows. These flows specify an ordering through a tokenwise time schedule that controls the relative denoising progress of tokens. We formulate a general class of asynchronous flows that admit both prescribed and learned, state-adaptive schedules, show that they define valid flow maps, and develop new methods for their flow map distillation. The resulting models can generate all tokens in a single forward pass by internalizing the ordering encoded in the flow into their hidden computations. On Sudoku solving and code generation for GSM8K math word problems, aFMLM establishes new state-of-the-art one-step performance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.42018 — 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 2026-11-02T06:57:13.233Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).