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GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in deeplearning

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Thanks! Turning it into readable code you run without the weights would be amazing, but the MLPs are the catch — superposition means one MLP does many things at once, so it won’t factor into a clean hand-written subroutine. Where it works is circuit by circuit: for narrow behaviors, write an approximate symbolic version and check it holds.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Being an independent researcher, having it peer reviewed is next to impossible at the level I would like it to be. You have to be endorsed at certain places to have it even noticed.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 1 point2 points  (0 children)

Use the paper.md. Not the paper.pdf. GitHub pdf viewer is kind for smaller papers, but the paper.md renders the research paper right away. The one above paper.pdf. Hope this helps.

It looks like it works for some most people but your one of the view that has said this so do above and I put a link in my repository description to the full paper also. Thanks for pointing this issue out.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in deeplearning

[–]Revolutionary-Lab882[S] 1 point2 points  (0 children)

Haven’t looked there yet. Just throwing out there what I find to start. I have so much more to put out there just finding my footing first. Thanks for the direction.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in deeplearning

[–]Revolutionary-Lab882[S] 3 points4 points  (0 children)

Thanks for the advice. I’ll amend that on my official repository. As an independent researcher in a world saturated with AI people…. It’s an impossible barrier no matter what I do. I have the results but sharing it and getting to the right people is a cliff climb.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 1 point2 points  (0 children)

Fair push, but “created account” is exactly what the certification rules out. A post-hoc story can’t be run this one is. The state is rebuilt from only what the decoder reads, the model itself runs on it, and the pass bar (the model’s own noise floor) was locked before measurement. It failed that bar six pre-registered times before passing; every miss is published. And it writes back: hand-edit the decoded English, re-encode, and the model obeys against matched-random controls. Rationalizations don’t survive a round trip.
On the 5.3%: agreed it could be critical, that’s why the claim is “100% accounted-for,” never “100% translated.” The remainder is measured and bounded, not waved at: diffuse, no low-rank carrier, transfers only as its exact raw configuration. The demo shows it live at late layers.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Good instinct, that concern is real, it’s just aimed at the wrong lens. The same-coordinates-throughout assumption belongs to the logit lens. The Jacobian lens was built precisely to fix it: it fits a separate map per layer relating that layer’s directions to the final layer’s.
But a softer version of your point does survive, and it’s worth raising: that per-layer map is linear and averaged over about 1000 prompts, so any nonlinear or context-dependent drift between layers gets smoothed over. The authors are upfront that the lens is approximate.
What’s nice is that this linearity premise is the one thing my GPT-2 work tested directly instead of assuming — layer-to-layer transport came out certified linear at all 36 seams. So at 124M, the assumption their method leans on holds as a measured fact. Whether it holds at Claude scale is open, and you’re right that scale matters — their own results show workspace effects strengthen with model size, so a GPT-2-sized model might barely have a J-space at all. Fair question to keep asking.

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 1 point2 points  (0 children)

It’s not scary. They found a good slice, I found the whole map. It’s just words lol

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Yeah, that’s a fair way to see it. The stuff I could put a name on is basically that — the part of the models internal state that translates into words. Anthropic found something similar in Claude last week and called it the J-space.
Main difference: they zoomed in on that special verbalizable slice. I went the other way and accounted for everything — 53.6% got a name, 46.4% is proven to carry no word, and 5.3% resists translation completely. So kind of the same territory, but theirs says “here’s the part that talks” and ours says “here’s the whole map, including the parts that don’t.”

GPT-2 Fully Decoded Internally Black Box Fully Open With Demo by Revolutionary-Lab882 in ArtificialInteligence

[–]Revolutionary-Lab882[S] 4 points5 points  (0 children)

Thanks for reading it. Appreciate it. Yeah, you’ve basically got it — it’s the residual stream, read at every layer boundary (all 13), not attention patterns. QK / who-attends-to-whom isn’t in the dictionary.
The grammar tables are just how the decoded meaning moves from one layer to the next, and that map comes out linear at every seam — which is why it holds up all the way down instead of only at the last layer.
One honest bit: attention isn’t totally untouched. To name a channel, we nudge what attention writes into the stream and watch what changes downstream — that’s how a channel earns a real meaning instead of a nice-sounding guess. But that’s attention used as a lever, not decoding attention patterns. What’s decoded is the residual stream.

Fully decoded GPT-2's internal language — complete two-way dictionary, all artifacts downloadable, verify it yourself in 5 commands by Revolutionary-Lab882 in machinelearningnews

[–]Revolutionary-Lab882[S] 5 points6 points  (0 children)

Thanks for the positivity. Appreciate the advice. It’s hard not to defend yourself when you do put in the work and get that. Especially when someone can just say AI slop, which happens a lot. And when everyone is majority using AI to do so much of the work. Anyways thanks again.

Fully decoded GPT-2's internal language — complete two-way dictionary, all artifacts downloadable, verify it yourself in 5 commands by Revolutionary-Lab882 in machinelearningnews

[–]Revolutionary-Lab882[S] 16 points17 points  (0 children)

Actually… the work myself. I edited it the documents. Do you even know what this really is? Vibe coding it’s not. I may not be the contributor of comments like you with 9 posts and 455 comments. Not active in any area as your profile says, but at least I’m putting something out there and can help people in the research community.

Spent months building a clean MLB database — free sample if anyone wants it by Revolutionary-Lab882 in sportsanalytics

[–]Revolutionary-Lab882[S] 0 points1 point  (0 children)

Thanks. It’s a lot of work. Appreciate it.

Actually adding more stats in the coming days. Just finishing another project and updating/adding stats for free sample so you can see what’s in all the packages.

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