Show HN: Eyesite - experimental website combining computer vision and web design (blog.andykhau.com)

Let's assume this is a paradigm shift on the scale of Transformers / `Attention is all you need`. Companies build out new models and pump another $100 Billion through it. And then a year from now, another innovation comes out. Same circus. And again.

No one wants to be left behind but trying to keep up will sink smaller companies.

gessha · 1d ago

Sir, this is an arxiv paper

NotAnOtter · 1d ago

So true, just like this one: https://arxiv.org/abs/1706.03762

curious_cat_163 · 1d ago

I am not sure why this ought to require "pump another $100 Billion". Could you elaborate?

Yes, the more recent generation of GPUs optimize for attention math. But they are still fairly "general-purpose" accelerators as well. So when I see papers like this (interesting idea, btw!), my mental model for costs suggests that the CapEx to buy up the GPUs and build out the data centers would get re-used for this and 100s of other ideas and experiments.

And then the hope is that the best ideas will occupy more of the available capacity...

ntonozzi · 1d ago

Is there any work related to using some kind of soft tokens for reasoning? It seems so inefficient to try to encode so much information down into a single token for the next pass of the model, when you could output a large vector for each forward pass, and have a drastically larger working memory/scratchpad, and have much higher bandwidth for the models to pass information forward to the next token call. If a single token has 17 bits of information, a vector of 1024 floats could have 32,768 bits of information.

ntonozzi · 14h ago

I just found a recent paper about this: https://arxiv.org/abs/2505.15778. It's really thoughtful and well written. They mix the different token outputs together.

Imnimo · 1d ago

This is an interesting way of squeezing extra feedback from raw text, but I'm a little skeptical that it's the best way to spend training flops. It feels like most "next tokens" are pretty low information (even after filtering for entropy like they do). Does it make sense to spend a bunch of compute on a reasoning trace on them? Maybe if you're harshly data limited, but not compute limited?

dgshsg · 1d ago

I notice that you can do this recursively to arbitrary depth. The cost is terrible though.

watsonmusic · 1d ago

it could be adaptive. only high-value tokens were allocated with more compute

hzia · 1d ago

This is very exciting! Existing data will become a lot more valuable and it brings it one step closer to how we learn as humans!

The downside is that this is going to be extremely expensive, so the data set to conduct RL will need to be curated.

watsonmusic · 1d ago

cannot wait seeing how it goes beyond the current llm training pipeline

nsagent · 1d ago

It's clear that you're either one of the authors or a friend of theirs. You created this account 8 months ago to comment on another paper [1] that was released by the same authors.

[1]: https://news.ycombinator.com/item?id=41776324

rafaelero · 1d ago

This should be used for high entropy tokens during pre-training.

babelfish · 1d ago

So marginally better (and occasionally worse) performance for an order of magnitude larger training costs…?

watsonmusic · 1d ago

14b model performs comparably with 32b size. the improvement is huge

85392_school · 1d ago

are we only comparing them in terms of text completion accuracy? does it also improve perf on benchmarks?

watsonmusic · 1d ago

A new scaling paradigm finally comes out!

beauzero · 1d ago

Interesting