# ChainFlow: Спекулятивті декодерлеу үшін азқадамды латентті ағым сәйкестіруі

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**Published:** 2026 ж. 9 қараша
**Author:** Selim Aktas, Yusuf Erdem Erdem, Ahmed Zeer, Himmet Toprak Kesgin, Muzaffer Kaan Yuce, Mehmet Fatih Amasyali
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/kk/articles/chainflow-few-step-latent-flow-matching-for-speculative-decoding

arXiv:2611.08669v1 Хабарлама түрі: жаңа Анотация: Спекулятивті декодерлеу LLM инференсін жылдатудың стандартты жолы болды: жеңіл дафтерші токендерді ұсынады, ал мақсат оларды бір параллел өткізуде тексереді. Мүмкін дафтершілер олардың жылдамаларын шектейтін келісімшарттарға ұшырайды:…

arXiv:2611.08669v1 Announce Type: new Abstract: Speculative decoding has become a standard way to accelerate LLM inference: a lightweight drafter proposes tokens that the target verifies in one parallel pass. Existing drafters face trade-offs that limits their speedups: autoregressive drafters generate tokens one at a time, so drafting cost grows with draft length, while diffusion drafters generate tokens in parallel but predict each position independently, ignoring inter-token dependencies. We introduce ChainFlow, a new speculative decoding paradigm and the first flow-matching drafter conditioned on the target's hidden states. ChainFlow treats drafting as a short transport over hidden states, needing one or two flow steps instead of the many denoising steps of generation from noise. A variational autoencoder compresses these states so drafting cost barely grows with target size, lightweight feedback heads restore inter-token dependencies, and tree drafting raises acceptance. On Qwen3.5 models, at matched drafter size and training data, ChainFlow outperforms EAGLE-3 and DFlash in vLLM by 39% and 11%, respectively. These gains further widen as drafters are adapted to new languages sequentially. Our ablations cover drafter design and concurrent serving, and we open-source our code and drafter models.

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*Материал бастапқы дереккөзден алынған: [arXiv — cs.AI](https://arxiv.org/abs/2611.08669). Түпнұсқаны ашу.*

**Тегтер:** #arXiv

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