FlashMap: Block Flow Mapping for Decoding

The paper introduces FlashMap, a method that utilizes flow maps to improve the speed–acceptance trade-off in speculative decoding, achieving a maximum speedup of 6.14x and a 7.4% average advantage over the DFlash baseline. FlashMap generates entire blocks of tokens in one network evaluation, maintaining joint distribution over tokens, and achieves stronger proposal quality with a two-step process, capturing inter-token dependencies more effectively. Experimental results on Qwen3-8B model show that FlashMap significantly improves drafting speed and proposal quality compared to autoregressive and block diffusion models, although improvements in proposal quality come at the cost of further computations. Problem formulation and central contribution Speculative decoding reduces autoregressive inference latency by delegating proposal generation to a lightweight drafter and verifying a block of proposed tokens with the target LLM in a single forward pass. Its practical benefit depends on two quantities: the computational cost of drafting and the expected accepted prefix length. Autoregressive drafters such as EAGLE-3 can achieve high acceptance rates, but their cost grows with the proposal length. Block diffusion drafters such as DFlash eliminate this sequential drafting bottleneck, but their one-pass factorization treats token positions independently. Consequently, individually plausible predictions can form jointly inconsistent sequences, causing acceptance to decay rapidly at later positions. "FlashMap: Flow Map Speculative Decoding" (2610.24365) addresses this limitation by replacing the factorized block-diffusion proposal with a continuous flow map over one-hot token embeddings. The drafter generates the entire block in one network evaluation while using a shared noise sample to model dependencies among positions. It retains DFlash's conditioning on hidden states extracted from the target model, but changes the generative mechanism so that the proposal distribution can represent a joint distribution over the block. The paper's principal empirical claim is that this modification improves the speed–acceptance trade-off. On Qwen3-8B, one-step FlashMap achieves the highest end-to-end speedup among the evaluated drafters on all nine benchmarks, with a maximum speedup of 6.14×6.14\times and a mean advantage of 7.4% over the matched DFlash baseline. Two-step FlashMap provides the strongest acceptance among the parallel drafters, improving accepted length over …