The paper shows that Test-Time Training (TTT) with key–value (KV) binding is not really memorizing like a notebook; it is acting like a learned linear attention layer.
The paper teaches large language models to learn from detailed feedback (like error messages) instead of only a simple pass/fail score.
This paper shows how a language model can keep learning while you use it, so it handles very long inputs without slowing down.