I'd like to see a math/logic bench appear for tokenization schemes that captures this. BPB/perplexity is fine, but its not everything.
https://arxiv.org/abs/2402.14903
You right to left tokenize in groups of 3, so 1234567 becomes 1 234 567 rather than the default 123 456 7. And if you ensure all 1-3 digits groups are in the vocab, it does much better.
Both https://arxiv.org/abs/2503.13423 and https://arxiv.org/abs/2504.00178 (co-author) both independently noted that you can do this with just by modifying the pre-tokenization regex, without having to explicitly add commas.
DAG architectures fundamentally cannot be AGI and you cannot even use them as a building block for a hypothetical AGI if they're immutable at runtime.
Any time I hear the goal being "AGI" in the context of these LLMs, I feel like listening to a bunch of 18th-century aristocrats trying to get to the moon by growing trees.
Try to create useful approximations using what you have or look for new approaches, but don't waste time on the impossible. There's no iterative improvements here that will get you to AGI.
> "You're not understanding, are you? The brain does the thinking. The meat."
> "Thinking meat! You're asking me to believe in thinking meat!"
https://www.mit.edu/people/dpolicar/writing/prose/text/think...
The Tree growing comment was a reference to another comment earlier in the comment chain.
The claim was that it isn't possible in principle for "DAGs" or "immutable architectures" to be intelligent. That statement is confusing some theoretical results that aren't applicable to how LLMs work (output context is mutation).
I'm not claiming that compute makes the m intelligent. I'm pointing out that it is certainly possible, and at that level of compute it should be plausible. Feel free to share any theoretical results you think demonstrate the impossibility of "DAG" intelligence and are applicable
The right alternative view is that it's an immutable function from prefixes to a distribution over all possible sequences of tokens less than (context_len - prefix_len).
There are no mutable functions that cannot be viewed as immutable in a similar way. Human brains are an immutable function from input sense-data to the combination (brain adaptation, output actions). Here "brain adaptation" doing a lot of work, but so would be "1e18 output tokens". There is much more information contained within the latter
It feels more interesting to note that this time, it is different. I've been watching the field since the 90s when I first dabbled in crude neural nets. I am informed there was hype before, but in my time I've never seen progress like we've made in the last five years. If you showed it to people from the 90s, it would be mind blowing. And it keeps improving incrementally, and I do not think that is going to stop. The state of AI today is the worst it will ever be (trivially obvious but still capable of shocking me).
What I'm trying to say is that the shocking success of LLMs has become a powerful engine of progress, creating a positive feedback loop that is dramatically increasing investment, attracting top talent, and sharpening the focus of research into the next frontiers of artificial intelligence.
90's? It's mind blowing to me now.
My daily driver laptop is (internally) a Thinkpad T480, a very middle of the road business class laptop from 2018.
It now talks to me. Usually knowledgeably, in a variety of common languages, using software I can download and run for free. It understands human relationships and motivations. It can offer reasonably advice and write simple programs from a description. It notices my tone and tries to adapt its manner.
All of this was inconceivable when I bought the laptop - I would have called it very unrealistic sci-fi. I am trying not to forget that.