@aamixsh
For anyone questioning whether this post is overhyped, I think they are actually *underselling* the approach.View quoted post on X
We did similar experiments and found that Claude models can write entire, functional neural net weights from scratch! csinva.io/weight-space-p…
The complexity theory result is actually huge.View quoted post on X
P problems can be solved in polynomial time. It was widely believed that NP-complete problems require exponential time.
P = NP states that NP-complete problems can be solved in polynomial time. OpenAI didn't prove that. But, they proved that (some) NP-complete problems can be solved faster than exponential time, meaning:
- P = NP, or
- the complexity of NP problems is a "middle thing" between polynomial and exponential, eg. O(n^log n)
If it survives peer reviews, this could be one of the biggest CS breakthroughs in decades.
We have never seen a model do this before, but GLM-5.3 sometimes reasons (unprompted) about how its current actions could become training data and impact its future self. 🧵View quoted post on X
