Signals 4 · free daily AI digest

The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation

Paper recorded by Signals 4 on 2026-10-14 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CL · 自然语言处理 · first seen 2026-10-15

Abstract

The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to scale memory use, as well. In particular, adding embeddings for n-grams scales memory separately from compute use, because each pass only processes individual embedding rows, which are much smaller than the storage used for the whole embedding tabl

Read on arXiv →

#250 most recent of 660 cs.CL papers we have recorded · ↑ newer: Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguit · ↓ older: SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent
Cite this page: The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation: the #250 most recent of 660 cs.CL papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/the-count-counting-n-grams-unlocks-scaling-on-host-memory-and-training-free-adap.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CL papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions