clearly a neural network can interpret and modify or add to another neural network to give it new skills, such as drawing ravens in this case. Opus 5.5 doesn't have some native modality for model weights, but it does have solid, superhumanly fast facility with the standard tools.
here's a KataGo with a grafted-on "Raven bypass" that, whimsically, makes it draw a raven with its stone placements, then continue playing normally.
inspired, I asked Opus to improve, via grafting, an older KataGo that struggles with a type of position, and it did. this isn't so surprising in that Opus is using standard, known interpretability techniques (linear probes, logit lens), and the bolted-on behavior is algorithmic.
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
Nathan dupes his subjects for sublime comedy but also his audiences for cheap drama. cf. The Rehearsal, season 2: Nathan doesn't sincerely care about aviation safety, yet he presents his (in)sincerity as a live question even metatextually. it's a meaningless, laughless schtick.


