Comment by tonii141
17 hours ago
a) That "no-tools" win depends on prompt orchestration which can still be categorized as tooling.
b) Next-token training doesn’t magically grant inner long-horizon planners..
c) Long context ≠ robust at any length. Degradation with scale remains.
Not moving goalposts, just keeping terms precise.
My man, you're literally moving all the goalposts as we speak.
It's not just "long context" - you demand "infinite context" and "any length" now. Even humans don't have that. "No tools" is no longer enough - what, do you demand "no prompts" now too? Having LLMs decompose tasks and prompt each other the way humans do is suddenly a no-no?
I’m not demanding anything, I’m pointing out that performance tends to degrade as context scales, which follows from current LLM architectures as autoregressive models.
In that sense, Yann was right.
Not sure if you're just someone who doesn't want to ever lose an argument or you're actually coping this hard