AI/ML Research Digest — Apr 11, 2026

LLM inference efficiency via adaptive routing, pruning, and hardware‑aware scaling Dynamic routing that selects full or sparse attention per layer cuts the cost of long‑context processing. Flux Attention implements this routing and delivers 2–3× speedups on benchmark tasks while keeping accuracy within a few points [1]. When routing is paired with token‑level pruning, the gains multiply. A task‑conditioned pruning network discards 92 % of input tokens that are irrelevant for the next action, y...