How TTT-E2E Lets Transformers Continuously Learn at Inference—A Plain English Guide
2 days ago 高效码农How to Let a Transformer Keep Learning While It Reads: A Plain-English Guide to TTT-E2E “ Keywords: long-context language modeling, test-time training, TTT-E2E, sliding-window attention, meta-learning, inference speed-up 1. The Problem in One Sentence Today’s best language models can open a book, but they cannot close it—they forget the first page before they reach the last. TTT-E2E, a paper posted on arXiv in December 2025, offers a different deal: read once, keep learning, and never pay more per new word. 2. A Quick Refresher (No Math Yet) What we already have Pain point Full attention Remembers everything, cost grows with …
How to Adapt Full-Attention LLMs to Sliding Window Attention: The SWAA Practical Guide
1 months ago 高效码农How to Adapt Full-Attention LLMs to Sliding Window Attention: A Practical Guide to SWAA Featured Snippet Summary Sliding Window Attention Adaptation (SWAA) is a practical toolkit for adapting full-attention pretrained large language models (LLMs) to sliding window attention (SWA) without expensive pretraining. It combines five methods—prefill-only SWA, sink token preservation, layer interleaving, chain-of-thought prompting, and fine-tuning—to reduce long-context inference costs to linear complexity while recovering most original performance on models like Qwen3 and Llama. Why Sliding Window Attention Matters for Long-Context LLMs If you’ve ever tried running a large language model on a really long prompt—say, analyzing a full book …