How TTT-E2E Lets Transformers Continuously Learn at Inference—A Plain English Guide
7 hours 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 …
The Assistant Axis Fixes LLM Jailbreaks: Why AI Models Break Character and How to Stop It
15 days ago 高效码农The Assistant Axis: Why LLMs “Break Character” — And How Researchers Are Fixing It Meta Description / Featured Snippet Candidate The “Assistant Axis” is a key direction in large language model activation space that measures how closely an LLM stays in its trained “helpful AI Assistant” persona. Deviations along this axis cause persona drift — leading to theatrical language, harmful suggestions, or successful jailbreaks. By capping activations on this axis during inference, researchers reduced persona-based jailbreak success rates significantly while preserving performance on major benchmarks (IFEval, MMLU-Pro, GSM8K, EQ-Bench). When you chat with modern large language models like Llama, Qwen, …
Automating Research Reproduction: How AI Code Generation Solves ML’s Biggest Crisis
9 months ago 高效码农Paper2Code: Automating Research Reproduction Through Intelligent Code Generation The Crisis of Unreproducible Machine Learning Research Recent data from top-tier conferences (NeurIPS, ICML, ICLR 2024) reveals a critical gap: only 21.23% of accepted papers provide official code implementations. This “reproducibility crisis” creates three major pain points: 6-8 weeks average time spent reimplementing methods manually 43% accuracy drop in unofficial implementations $2.3B estimated annual loss in research efficiency globally Traditional code recreation faces fundamental challenges: Ambiguous specification gaps between papers and implementations Hidden dependency chains requiring iterative debugging Undocumented hyperparameter configurations Introducing PaperCoder: A Three-Stage Solution Developed by KAIST and DeepAuto.ai researchers, …