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ChainFlow: Few-Step Latent Flow Matching for Speculative Decoding

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/11/2026, 05:03:25 UTC.

Briefings

ChainFlow introduces a speculative decoding paradigm using flow matching conditioned on target hidden states, achieving 39% speedup over EAGLE-3 and 11% over DFlash on Qwen3.5 models. The method compresses hidden states via a variational autoencoder and uses tree drafting to restore inter-token dependencies, significantly reducing drafting cost relative to target size.

Key points

• ChainFlow achieves 39% speedup over EAGLE-3 and 11% over DFlash on Qwen3.5 models using flow matching conditioned on target hidden states.
• The method uses a variational autoencoder to compress hidden states, reducing drafting cost as target size grows.

Context

Speculative decoding accelerates LLM inference by having a lightweight drafter propose tokens verified in parallel. Existing methods face trade-offs between autoregressive and diffusion approaches. ChainFlow represents the first flow-matching drafter conditioned on target hidden states, treating drafting as a short transport over hidden states.

Limitations

Results are limited to Qwen3.5 models with matched drafter size and training data. Performance gains widen when drafters are adapted to new languages sequentially. The study covers ablations on drafter design and concurrent serving but does not include results for other model architectures or hardware configurations.

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