arXiv Computation and Language November 2, 2026 By Chanhyuk Lee, Wonjung Kim, Patrick Pynadath, Jaehoon Yoo, Satoshi Hayakawa, Ruqi Zhang, Jinwoo Kim, Seunghoon Hong

Asynchronous Flow Map Language Models

Read the original on arXiv Computation and Language →

The paper introduces asynchronous flow map language models (aFMLMs), a new one‑step parallel language modeling approach that incorporates token prediction order via learned or prescribed asynchronous flows. These flows define a token‑wise time schedule controlling denoising progress, and the authors prove that such flows yield valid flow maps and develop distillation methods. Experiments on Sudoku solving and code generation for GSM8K math word problems show that aFMLMs achieve state‑of‑the‑art performance in one‑step generation.

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