SubQ: a sub-quadratic LLM with 12M-token context (opens in new tab)

Assuming this is real and much better than existing linear attention methods as advertised, not launching with a technical report is a big miss.

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.

- 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.

I hope this is real but I doubt...

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.

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.

Whether this is real or not, multiple commenters here look like astroturfers - created in the past year (or hours) with very low karma

I wonder how different their method actually is from other sub-quadratic sparse attention methods like Reformer [1] and Routing Transformer [2].

[1]: https://arxiv.org/abs/2001.04451

[2]: https://arxiv.org/abs/2003.05997

This is pretty remarkable. We've spent a lot of time finding workarounds for LLMs reading long docs. Now that's gone.

I'm usually okay with most LLM-assisted writing, but the amount of "it's not X. it's Y" style of phrases in https://subq.ai/how-ssa-makes-long-context-practical is disturbing.

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.

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.

> 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.

Let me know if I misunderstood

Looks like long context isn’t a problem anymore

No API access for independent verification - vaporware. See also comment about astroturfing accounts in this thread.

An architecture where compute grows linearly with context length seems dangerous. It can get very expensive as context grows and performance degrades