> A slightly smarter highschooler could write these. I could write these. It's clear as day that the LLM, not the human, did the heavy lift. It'd be ridiculous to give full credit to whoever wrote the prompt.

I think you're over-estimating what a smarter highschooler could write.

A "finite loopless undirected multigraph" could have been explained to me at that age if we'd taken Discrete rather than Mechanics and Pure (and one module of Stats) in my two A-levels* in maths and further maths; but from what I saw of the Discrete module, neither:

  Every finite loopless multigraph with no bridge possesses a cycle double cover, without additional assumptions such as cubicity, planarity, connectivity, or higher edge-connectivity.

  repeated-edge closed trails masquerading as cycles

* For those not from the UK: A-levels are between secondary school and university, when aged 16-18. Functionally they are university entrance qualifications: https://en.wikipedia.org/wiki/A-level_(United_Kingdom)

The human provides the intention and the ability to appreciate the output. Tools do “heavy lifting” all the time, but we still primarily credit the humans who use them precisely because they made the choice to use them.

Provability is just going the way of computation. John Napier had to manually compute logarithm tables over decades and was recognised for his work; now that same work could be performed by a 10 year old with a calculator in an evening.

But why can’t we prompt the LLM “just do math research”? This is what I don’t understand.

When you use a crane to do the "heavy lifting" for construction work, do you give full credit to the cranes?

I don't disagree with you but there's no need for exaggeration; ain't no high school student writing this:

> In particular, proofs for special graph classes, constructions of cycle covers with some edges covered other than twice, bounded-length or prescribed-cycle variants, reductions to another unproved conjecture, computational verification through any fixed graph size, and candidate counterexamples without a complete nonexistence certificate are insufficient.

which is infact a very important part of the prompt.

I'd say the creator is the one who created the 3d model, not the one who pushed the print button.

I honestly don't think a language model is enough for self-awareness, regardless of the exact model of awareness.

A language model (or an image model or whatever) cannot even be sentient, and I think sentience is a prerequisite for awareness.

Even if we express a lot of our subjective experience with words, the language is just a symbolic representation of those experiences. The qualia themselves, even those that are quite abstract, are rooted in our physical presence and evolution.

You can't have an understanding of what hunger or physical pain feel like if you have no need for food or a sensory capacity for feeling pain. You can't understand what loneliness or pride at an achievement mean if you don't have a neural network wired to value social connection or status. We value connection because we're social animals that have needed each other for survival.

Even the more abstract of our subjective experiences are in some way rooted in our physical evolution.

I see no reason to believe that a neural network built entirely based on the symbolic level of language could have the features needed for the subjective experience itself.

AI awareness might actually be more believable if that awareness manifested itself in an entirely different way than in humans. But if we assume awareness because outputs resemble what we consider meaningful as humans, yet the neural network has had no inputs or evolution that could form the actual basis of human-like experience, I think we're seeing something that isn't actually there.

Dunno about the parent commenter, but I personally interpret the concept as having a hidden representation of self that is continually tended to, and influences future choices. This implies statefulness, which models are intentionally not at inference time (*).

(*) Even if we hack around this and just do the usual trick of simply laundering statefulness to a higher level, in this case the context window being fed in, I fail to identify (**) a representation of its own state in these bodies of text that it'd be meticulously maintaining. I further fail to identify how it could be hidden or maintained, considering I control like half of it. The best you could ascribe it is a meticulous maintenance of a persona the user is talking to, but then that doesn't necessarily represent the model's internal state, the same way my own words here aren't doing so either. Difference being, I actually have one (I'm "on-line").

You'll sometimes catch models mixing up who's who and how many who-s there even are for example.

(**) I did wish for something hidden though, so maybe it's just concealed? The same way people can encode a lot more of their emotional and mental state than normal into text if they read and write a lot of it, I'm aware of research that suggested the same for LLMs, albeit I cannot cite it. Maybe those phrasing signatures are just alien to me and will never pop out. Either way, I'd expect researchers to stumble upon this during interpretability studies, and either they haven't, they have but it wasn't popsci adopted, or they're keeping awfully tight lipped about it. If you know of anything like this, your turn now, would be happy to learn.

I do wonder how reasonable it is to expect e.g. a single maintained identity though. Maybe it isn't?

(*) Another way to hack around this of course is to just precompute some internal "self-awareness states" and hop around between them. Probably the closest to what the models are actually "doing".