AI Model Learns While Reading
Published:Jan 2, 2026 22:31
•1 min read
•r/OpenAI
Analysis
The article highlights a new AI model, TTT-E2E, developed by researchers from Stanford, NVIDIA, and UC Berkeley. This model addresses the challenge of long-context modeling by employing continual learning, compressing information into its weights rather than storing every token. The key advantage is full-attention performance at 128K tokens with constant inference cost. The article also provides links to the research paper and code.
Key Takeaways
Reference
“TTT-E2E keeps training while it reads, compressing context into its weights. The result: full-attention performance at 128K tokens, with constant inference cost.”