MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM — 2026-05-27

Impact Vector: AI Tools · May 27, 2026 · 2m

## Short Segments Speculative decoding just got a major reliability boost with EAGLE 3.1, fixing attention drift in LLM inference. Today, we're diving into how EAGLE 3.1 enhances speculative decoding, a technique that speeds up large language model inference by using a small draft model to propose tokens, which the larger model then verifies. While previous versions struggled with attention drift, EAGLE 3.1 introduces per-layer normalization and a post-norm feedback loop to stabilize performance. This upgrade means up to twice the acceptance length and throughput, depending on hardware and…