How TTT-E2E Lets Transformers Continuously Learn at Inference—A Plain English Guide
20 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 …
Neural Memory Agent: Differentiable Memory & Meta-Learning for Lifelong AI Systems
3 months ago 高效码农Building Neural Memory Agents: A Hands-On Guide to Differentiable Memory, Meta-Learning, and Experience Replay for Lifelong Learning in Changing Environments Ever wondered how an AI could juggle multiple skills without dropping the ball on what it learned before? Picture training a model that remembers your first lesson on image recognition while swiftly picking up voice commands—no more starting from scratch every time. That’s the promise of neural memory agents. In this practical tutorial, we’ll roll up our sleeves and build one from the ground up using PyTorch. We’ll weave in differentiable memory for smart storage and retrieval, meta-learning for quick …