Laguna XS.2 and M.1

I like their honesty in benchmarks, looks like Qwen3.6 35B is outperforming their Laguna M.1 225B model

  Laguna XS.2  33B-A3B params: 30.6
  Qwen 3.6     35B-A3B       : 51.5
  Devstral 2   123B          : 31.2

Also, *ops work, which in my experience can actually be more complicated than SWE is underrepresented there obviously.

I usually score pretty well in colour perception tests but distinguishing between those two purples made me doubt myself.

One nit: I've seen on this homepage, and many others, this notion that the people behind the models are "working towards AGI".

I get that this is marketing speak, but transformers are not AGI, and they will never be AGI, so it'd be great if people stopped saying that as it sort of wears out the meaning of "working towards AGI".

Transformers have approximate knowledge of many things. Is this not 'general'? Where is the goalpost here?

Of course not. That's like saying the Encyclopedia Britannica is AGI.

> What does AGI mean to you?

I would define AGI as human-like machine intelligence (or superior).

This is difficult for some people to understand because they don't understand what "human-like" means in the first place. Neuroscientists would be able to set some of these wayward computer scientists straight on this question.

But is that a hard requirement? Can a machine have Rat-like intelligence? Is all intelligence human-like (human-centric-mind-blindness-much?)?

> Of course not. That's like saying the Encyclopedia Britannica is AGI.

Well, I'd classify that as GK, general knowledge. Not artificial or intelligent.

Let's consider a definition of intelligence as the act of 'manipulating data', have you a better general definition of intelligence?

Yes.

> Can a machine have Rat-like intelligence?

Yes, and that would be closer to AGI than today's LLMs, because the fundamental principles and architecture is there.

What makes you think that? That there are no other patterns of intelligence?

The principles that are found in the brain are what gives qualification to "AGI", not the brain itself, so it's possible there are other architectures that would qualify.

A few observations on LLMs that give the game away:

- They require releases. You get a single binary blob and that blob is forever stuck at its so-called "intelligence" level. It never learns anything new.

- They're stuck approaching the limit of human intelligence. This is because the technique cannot exceed human intelligence. I realize that OpenAI has made claims to the contrary, saying things like "oh our model found out some proof that was never proven before" — this doesn't count. It's a side effect of training on the Internet. In fact that proof probably did exist (in pieces) somewhere on the Internet, it just wasn't widely publicized.

So, you'll know it's AGI when you no longer see companies releasing new models. AGI won't require new models because the architecture will be what matters as whatever models you have will be constantly updating themselves in real-time, just like the human brain does (and every other brain).

And, you'll start to see the AIs actually outsmarting the smartest humans on the planet in every subject.

True. But learning isn't the same thing as intelligence. My father who has dementia and is unable to learn anything new due to memory issues is still 'intelligent'.

> - They're stuck approaching the limit of human intelligence.

Is general intelligence > human intelligence then? Is there some static 'human level' that I should be measuring myself against?

There is considerable overlap between the smartest bear and the dumbest human. same is true with LLM's and humans how.

What you seem to be describing isn't AG(eneral)I, but artificial greater intelligence.

If you ignore what I said in answer to you earlier then perhaps it would make sense to draw this conclusion. But if you take the full context of what I said then no, it's clear that I am not referring to "artificial greater intelligence".

Just in the previous comment I said that rats would qualify, because the architecture is what matters.

Your example with dementia is clever but that's an example of the biological architecture breaking down. Please forgive the crude analogy but it's like asking if a house is still a house if it's been burned down partially. I suppose part of it is still a house.

Spirits are considered intelligent even though they have no body because they are composed of pure non-physical consciousness. Plants are intelligent even though they also have no brain.

That fundamental sort of living conscious intelligence isn't what I see discussed much in these contexts though.

What you will notice about it though is that unlike frozen LLMs, this type of intelligence also has the capacity to change, interact, and learn from its environment.

If we go with this definition instead, then on a large enough timescale everything can be considered intelligent, even rocks.

I blame it on the big companies in the space, but seeing intelligent folks regularly attributing intelligence to a complex autocomplete system is disappointing.

Like the claim "transformers are AGI", this needs proof, otherwise should be prefixed "I think". And honestly, positive proof is easier than negative proof (you just need to make one transformer model that is a AGI, whereas the never claim requires you to enumerated all possibilities).

The negative proof is there in the definition itself. Transformers are not AGI, they're frozen human intelligence of the autocomplete variety. That can never be AGI and anyone who says otherwise doesn't understand transformers or AGI.

So yes, the LLMs can be trivialized as just randomized autocomplete, but if you add a database or memory to the side very basic MLPs can become a Turing machine. It's going to take a lot more proof to say a Turing machine could never be intelligent. And you can do more than just give the LLM side memory - you can have them invoked recursively, use message passing as coroutines, and so on...

You might be technically correct if you ignore anything other than the very restrictive definitions you're using, but even there I'm not certain. If you had a LLM with a trillion token window, is that good enough to act as a memory? Human brains aren't infinite either.

Interestingly, Gemma 4 26B-A4B and Qwen3.6 27B (dense) have been left out of the comparison.

The smaller models are becoming very good and quantization techniques like importance weighting and TurboQuant on model weights let you run aggressively quantized version (IQ2, TQ3_4S) on consumer hardware with extremely acceptable perplexity and quality loss.

Very exciting times for local LLMs.