Adaptive computation for efficiency

Block‑sparse attention scheduling cuts inference latency by up to 60 % without hurting quality; the ALLOT scheduler achieves this by allocating sparse blocks across layer‑step pairs [1] . MambaAct modules apply a similar adaptive principle to vision‑language‑action pipelines, letting agents skip unnecessary computation steps [2] . In diffusion language modeling, SpecKCast generates speculative drafts that let the model bypass many denoising iterations, delivering up to 2.5× higher throughput while keeping output fidelity [3] . Four‑bit state quantization compresses attention memory and linear‑attention kernels, halving the memory bandwidth required for long contexts [4] .…