Some thoughts about Anthropic's new cryptanalysis results(blog.cryptographyengineering.com) |
Some thoughts about Anthropic's new cryptanalysis results(blog.cryptographyengineering.com) |
> If you’re under the impression that these models are “glorified autocomplete” or that progress is slowing down, I need to urge you: stop thinking that. The models are very intelligent and capable, they are getting better at a fast clip. I can cite measurable and impressive progress over just the past five months on specific types of problem I’ve asked them to look at. [...]
> On the other hand: if you think that models are super-intelligent or that AGI is already here, you should also stop thinking that. Working with these tools is like swimming in a pond where the ground drops off sharply. One minute you’re wading comfortably and there’s support under your feet. Then suddenly you cross a specific line, and you’re back to swimming on your own.
That sentence gives the impression that Mythos might be released in the future. That's clearly not going to happen - it's already "released" in as much as selected, trusted partners can access it, and the rest of us get it in the form of Fable - which is Mythos but with filters that downgrade you if you try to use it for anything even remotely related to cybersecurity or biology.
(The other day Fable 5 downgraded me to Opus after I asked it to explain the difference between tusks and teeth.)
it is fun how well this works.
i cant find the link immediately (will look and edit with it), but somewhere in the "hello there the jacobian conjecture is false thanx" thread, someone brought up a different conjecture breakthrough where the prompts were basically just repeated "no, keep going" until a result was found.
edit: https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...
i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.
did i just read two summaries/TLDRs (in a row) of the already-two-sentence summary right above?
This is well past prompt engineering and into process engineering like six sigma. Just like in an early industrial revolution factory, we’re all still figuring out what works in the process of making stuff except this is so early that even simple things like “make this screw standardized” (or “no, keep going” in this case) is really high impact.
The degrees of freedom an LLM has is so large that we're going to be exploring their capabilities for decades, especially if they continue to get better. This is why IMO experts are always going to be better at LLMs in their field because they can force them LLM into processes (think prompt engineering -> CC dynamic workflows) that follow their work processes and get much better results out of them than “keep going.”
in my head, the comparison is the multi-paragraph prompts (borderline essays) i would read in various communities on reddit and similar forums, that people (often self-proclaimed "prompt engineers") said were "required" to get good output. or some of the prompts ive read in various logs that are like a thousand words of setup.
even looking back at the first prompts i was sending when i started to use chatgpt were (in hindsight) crazy long and full of unnecessary guidance/caveats/"ignore xyz"/etc.
Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts like "you should do a breakthrough", knowing that the model is capable of solving this particular one. Worst case the meme-prompts don't work and they share the real one they initially used.
i am not sure why this is "quite likely". it'd be pretty silly to get a mathematical breakthrough and then hide it for an undisclosed amount of time to get a few more likes on a tweet, when the impressive part is the breakthrough.
not saying your theory is impossible, but i think the simple answer is that the model is just smarter than o1 and o3.
and, in any case, the model ended up getting the result with the meme prompt and "keep going", which was what i find fun. just like how the crypto results were from prompts of, more or less, "keep going", and that's pretty damn cool.
Personally I like to remind people that these things are next-token predictors, but then emphasize how truly astonishing the results we can get out of sufficiently advanced next-token predictors are.
> The tokens it "predicts" aren't sampled from any naturally occurring distribution; the model's output is the result of an optimisation process that rewarded behaviour that was useful, and that's fundamentally different.
"Surprisingly, we find that the current training setup does not elicit fundamentally new reasoning patterns. While RLVR-trained models outperform their base models at small k (e.g., k = 1), the base models achieve a higher pass@k score when k is large. Coverage and perplexity analyses show that the observed reasoning abilities originate from and are bounded by the base model. "
You can get into RL as part of explaining why it's so unnervingly good at picking a next token.
It's harder to believe something is conscious or threatening to achieve word domination once you understand that it's a machine that statistically figures out which word should come next.
I guess it was more the "predictor" part I had issue with. There's a tendency to reach for statistical or probabilistic terminology to describe things that aren't usefully understood in those terms. For example in the "Speed Always Wins" LLM technical survey (https://arxiv.org/pdf/2508.09834):
> The gate is a crucial component to bring sparsity in MoE models. For a batch of input token representations X ∈ RT×D, the gate function G determines the probabilities of dispatching token xi to each expert e
...which is nonsense: the gate simply, directly, selects the experts. There's nothing probabilistic about it.
I agree with you that obviously no prompt engineering was needed just "solve this problem", but imagine it was you doing this problem with every model, wouldn't you have tested a new model with the best prompt you had from previous iterations, maybe with some partial previous results in it, exactly to maximize your probability for a mathematical breakthrough?
I recommend this explanation: https://www.astralcodexten.com/p/next-token-predictor-is-an-...
(Note: I don't actually think the consciousness question is the most important one in the near term. Where I think this line of reasoning gets really dangerous is when people use it to assert that LLMs can't or won't engage in certain behaviors no matter much they advance; this doesn't have anything to do with consciousness.)