Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding

arXiv:2606.24957v1 Announce Type: cross Abstract: While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a verification bottleneck where Key-Value (KV) cache loading dominates latency. Existing compression methods fail in this regime: static eviction incurs accuracy loss due to saliency shift, while dynamic selection introduces prohibitive computational overhead during the verification path. We propose Dustin, a sparse verification framework designed for long-context speculative decoding. Dustin int

Source: arXiv cs.LG — read the full report at the original publisher.

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