Jul 2026
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
SpecLA is presented, a speculative decoding runtime for stateful linear-attention models that verifies chains and trees with topology-aware kernels, stores compact factors produced during verification to recover accepted states, and uses confidence pruning plus a target-aligned EAGLE-style drafter to feed useful candidates to the verifier.
Zhibin Wang, Xuying Han, Zhaohua Yang et al.
· arXiv.org · 1 citation