I’m very surprised this isn’t getting more attention. Am I missing something?
It seems at or above SOTA on the given benchmarks, doesn’t have context rot, is orders of magnitude faster, and uses less compute that current transformer models. I suppose it’s just an announcement and we can’t test it ourselves yet.
Edit 2: the fact that they're going straight for an end-to-end coding product on day 1 is very ambitious. Other speed/efficiency-oriented AI companies (Cerebras and Inception come to mind) still don't have a first-party coding product after years. IMO this is absolutely the right way to go if they really do have the big breakthrough they're claiming.
- magic.dev claimed 200M context window and it's been two years since and no real product yet.
- They are admitting that this is built on top of a Chinese model[1]
- They committed a huge chart crime with the Y axis of a chart comparing to Opus on their website that I can't find anymore (Too embarrassing to keep?). The delta between their score (81%) vs. Opus (87%) on SWE bench was hugely minimized
- They named the company subquadratic but in parts they said O(1) linear scaling. At O(1) you could do much more than 12M tokens context window. At O(log n) even.
The chart crime was not intentional! We will not make you wait two years. We are O(n), not O(1). O(1) would unfortunately be an impossibility. We may as well do infinite context at that point!
i see in the linked post they mention O(n) not O(1). O(1) would basically be impossible and instant. Something like no compute required, constant results...
The name subquadratic is actually good and makes sense to me. Because today's models are usually O(n^2) or worse. Anything equals or less than O(n^1) is basically sub-quadratic.
Meanwhile O(log n) would be logarithmic as the log name indicates. But we have a long way to go there. Maybe with double tokenizer plus extensive caching it may be possible...
What I mean here is tokenizing the user input; then capturing intent; caching intent -> response. So that next time once you get the intent, you don't need to do full transformer inference compute. This can be logarithmic complexity in terms of time complexity.
> The core idea is content-dependent selection. For each query, the model selects which parts of the sequence are worth attending to, and computes attention exactly over those positions.
I don't know if this will help for things like understanding code, where the all relevant parts can be the file of 1000 lines that we are analyzing, and where every token is relevant in understanding recursion, loops, function calls, etc.
This sounds like it would be great to do SSA before passing things along to a code model like claude code.
Funny how they claim a 12M context window, yet all benchmarks are cherry picked with a 1M context window. Also, nobody has questioned how they did a training run before receiving funding. SoTA training runs cost well above $10M, yet no mention of funding prior to yesterday, interesting.
Don't let a C-suite marketing video blow your mind. They are trying to discover the new Transformer, that's not easy. 12 million token context with worse quality means this isn't going anywhere. Want to bet me bitcoin that we won't be talking about them in 1 year? Heck, they may have found something great, but the prior should be one of skepticism.
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[ 5.1 ms ] story [ 61.1 ms ] threadIt seems at or above SOTA on the given benchmarks, doesn’t have context rot, is orders of magnitude faster, and uses less compute that current transformer models. I suppose it’s just an announcement and we can’t test it ourselves yet.
Yes, this product doesn't exist.
And the last time a company claimed something similar it disappeared after taking money from investors.
Edit: their blog post (https://subq.ai/how-ssa-makes-long-context-practical) does go pretty in-depth about it
Edit 2: the fact that they're going straight for an end-to-end coding product on day 1 is very ambitious. Other speed/efficiency-oriented AI companies (Cerebras and Inception come to mind) still don't have a first-party coding product after years. IMO this is absolutely the right way to go if they really do have the big breakthrough they're claiming.
- They are admitting that this is built on top of a Chinese model[1]
- They committed a huge chart crime with the Y axis of a chart comparing to Opus on their website that I can't find anymore (Too embarrassing to keep?). The delta between their score (81%) vs. Opus (87%) on SWE bench was hugely minimized
- They named the company subquadratic but in parts they said O(1) linear scaling. At O(1) you could do much more than 12M tokens context window. At O(log n) even.
I hope this is real but I doubt...
i see in the linked post they mention O(n) not O(1). O(1) would basically be impossible and instant. Something like no compute required, constant results...
The name subquadratic is actually good and makes sense to me. Because today's models are usually O(n^2) or worse. Anything equals or less than O(n^1) is basically sub-quadratic.
Meanwhile O(log n) would be logarithmic as the log name indicates. But we have a long way to go there. Maybe with double tokenizer plus extensive caching it may be possible...
What I mean here is tokenizing the user input; then capturing intent; caching intent -> response. So that next time once you get the intent, you don't need to do full transformer inference compute. This can be logarithmic complexity in terms of time complexity.
I don't know if this will help for things like understanding code, where the all relevant parts can be the file of 1000 lines that we are analyzing, and where every token is relevant in understanding recursion, loops, function calls, etc.
This sounds like it would be great to do SSA before passing things along to a code model like claude code.
Let me know if I misunderstood
[1]: https://arxiv.org/abs/2001.04451
[2]: https://arxiv.org/abs/2003.05997
Also, holy moly, the astroturfing.
But I'll still keep an eye on what they'll show up with in the next months. Sounds intriguing.