E-Reverance

Opus 5.5 came out around a month ago, I'd give it some time before coming to that conclusion

terrible quality video my friend sent but the idea seems genuinely good and would like to hear if hner's have done something similar before

Schmidhuber : https://arxiv.org/abs/0812.4360

Or we can finally decouple intelligence/ability and from (social) worth

Why do all the "frontier" models still occasionally output images with the horrible ai texture tells? I'm surprised whoever made the front page let those ones in lol. My hypothesis is that it is either an unpatching or feature loss shortcut/artifact. Until then I have no hopes of ai generated images being viable for anything actually beautiful or creative

Ex. https://optim-images.krea.ai/https---s-krea-ai-k3-presets-20...

I loved to hear! Just ping me on twitter (same account I sent the link with)

I never said its unpleasant, what I'm meant math in its current (social) form / interface is "solved"

1) yes, but with more than channels than 3 (and no correspondence to color, its "pixel space" in the sense of position, not value)

1D grid example for clarity (obviously meant to be done in 2D though)

So embed -> [N dim, N dim, N dim ... , N dim] instead of embed -> [RGB, RGB, RGB ... , RGB]

2) / [followup 1)] Stride of 1, so we place a window at each pixel (so lots of overlapping)

Also I wouldn't phrase it as "come up with a better image", the point is to give less spatial decoding pressure to the patch tokens so that they can almost completely focus on feature learning instead. There is no reason to have the model learn spatial decoding when the structure prior of images is comically strong (especially compared to text), its a waste of training time and parameters

[followup 2)] I haven't measured but it was passable is all I can say (my experiments setup are horrendous right now lol)

(Also don't mind the phrasing, I just wanted to be 100% clear)

But who is going to be the one solving them? Just query an LLM no?

I know it goes a tiny a bit against the spirit of what y'all are doing, but applying a few layers of pixel-wise local attention (so 1x1 "patch", with 3x3 or 5x5 attention window, basically treating it as a dynamic conv) has worked way better than both linear and conv unpatching in my recent experiments.

Diagram for reference https://x.com/1rreverant/status/2107546198093730287 (In my most recent recent experiment I actually removed the MLP and just used a linear project on the pixel's hidden states)

> You don't have ADHD or autism, you just suffered from trauma.

Yeah that's exactly what I'm saying, but more specifically I wanted to relieve myself of blame through a label my parents could more easily empathize with

> A diagnosis of ADHD or autism in adulthood often leads to incredible relief and grief in processing all the moments of being blamed and shamed for what was assumed to be a lack of effort, laziness and/or defiance.

This is definitely a factor for me when I tried to get diagnosed (which failed lol), because my parents whom caused my trauma would obviously refuse to blame themselves for my emotionally stunted development

What AI if is the only means for having those in your lifetime

Scale with compute*

not explicit to reference scaling laws of training