lukev

Humans working closely in the same field also have a "warped and compressed" language, we call it "Jargon".

I think we're in violent agreement here. Language is part of how we think. Definitely not the whole thing -- we are physically and biologically instantiated and LLMs are not. Language is a software, but the hardware does matter.

But LLMs DO show us that language is a big deal. It isn't just a serialization protocol for real thought -- it's a material component of how we think.

In many ways I think LLMs are the strongest possible proof of structuralist and post-structuralist theory which argues that human cognition is conditioned on the language and concepts to which we have access.

LLMs are showing us that a lot of what we think of as cognition is actually part of the elaborate symbol system which is Language, as opposed to a property of "mind" (either biologically or artificially instantiated.)

LLM-generated text is hard to understand because while it's perfectly good language that follows valid cognitive and informational patterns, it does not reflect the intent or mental model of another mind.

LLMs absolutely do not have a logic detached from humanity. They are exactly the opposite. The only mode in which they can reason is by following the patterns obtained from their pre-training or weighted by RLHF.

The reason these models answered that question consistently is that the question itself pushed them towards the latent space of "intellectualizing about what's wrong with humanity at the meta level" and when you frame the question that way, that's also the most common answer for humans writing about the topic.

If anything, claiming that this vulnerability has been known and unpatched for 22 years supports the author's point: clearly it's not that easy to actually use.

I’m sorry, perhaps I’m behind the times here, but do frontier models currently use continuous (arbitrary embedding) thinking tokens? I know a bunch of papers on the topic came out late last year but to my knowledge have not been successfully implemented into the SOTA yet.

I know the biggest/best open models don’t use continuous thinking tokens, and while the big labs keep things proprietary, I don’t think we have evidence yet that they are.

If I’m wrong please point me in that direction, I’d love to read up on it. Using continuous embeddings in CoT has always seemed obvious to me but doesn’t seem to be as simple as that.

Jev is interesting in that it's much cheaper and faster than a frontier LLM.

But I've seen nothing to indicate that the upper bound on classification tasks of a Jev-like model can exceed a frontier LLM with reasoning tokens. That seems nearly impossible even in principle (since Jev-style models are still based on LLM pretraining).

So while they're definitely on the Pareto frontier, which is valuable, they're at the "cheap" end of the spectrum more than the "good" end and I don't expect that to change.

Begun, the AI SEO wars have.

From the report:

> Because there were over a thousand transcripts and most were extremely long, we had to heavily delegate our analysis to AI agents; these agents had significantly worse judgment and reliability than human researchers, and it was challenging to spot check their work because both the underlying data and the agents’ analysis of it was often difficult to interpret.

> We estimate we spent roughly ~$400K in API credits over the six days of our investigation.

I don't understand why you think it's conceptually absurd? I use agents to analyze complex production issues all the time and they are very much capable of hallucinating a narrative.

The elephant in the room here is that the METR report itself was researched and compiled almost entirely by AI, with only very limited human "spot checks."

So I'm really not sure how much of it can be believed, especially since AI agents are strongly biased about the capabilities of AI agents.

Well, it’s mentioned as a limitation of the analysis, very much not ruled out (or in.)

That simonw is causing labs to do extra fine-tuning runs for this seems highly probable :)

What if they’re not pelicanmaxxing, but svgmaxxxing in general?

Because otherwise using a LLM to generate complex svgs is pretty niche and what I thought made this a good benchmark when it was new - generalized programming and spatial knowledge.

Obviously image gen in svg format is not a particularly hard problem if tackled directly on its own.

You get calls about a new service or promotion, and it's the diction of the caller that makes you not wish to engage...?!

Counterpoint: the standardized surface area of a browser is already enormous, and while these components seem simple, there are a billion different options, variables or alternative implementations to consider.

At some point, functionality needs to exist in user space, even if it's common.

What was the “well defined” definition? I’m not aware of any other than “this particular thing a human can do that I expect would be difficult for a computer.”