[bradly]

What is really interesting is in the "thinking" section it said "I need to reassure the user..." so my intuition is that it thought it was right, but did not think I would think they were right, but if they just gave me the confidence, I would try the code and unblock myself. Maybe it thought this was the best % chance I would listen to it and so it is the correct response?

[TeMPOraL]

Maybe? Depends on what followed that thought process.

I've noticed this couple times with o3, too - early on, I'd catch a glimpse of something like "The user is asking X... I should reassure them that Y is correct" or such, which raised an eyebrow because I already know Y was bullshit and WTF with the whole reassuring business... but then the model would continue actually exploring the question and the final answer showed no trace of Y, or any kind of measurement. I really wish OpenAI gave us the whole thought process verbatim, as I'm kind of curious where those "thoughts" come from and what happens to them.

[ben_w]

Not saying this to defend the models as your point is fundamentally sound, but IIRC the user-visible "thoughts" are produced by another LLM summarising the real chain-of-thought, so weird inversions of what it's "really" "thinking" may well slip in at the user-facing level — the real CoT often uses completely illegible shorthand of its own, some of which is Chinese even when the prompt is in English, but even the parts in the users' own languages can be hard-to-impossible to interpret.

To agree with your point, even with the real CoT researchers have shown that model's CoT workspace don't accurately reflect behaviour: https://www.anthropic.com/research/reasoning-models-dont-say...

[andrepd]

Okay. And the fact that LLMs routinely make up crap that doesn't exist but sounds plausible, and the fact that this appears to be a fundamental problem with LLMs, this doesn't give you any pause on your hype train? Genuine question, how do you reconcile this?

> I really wish OpenAI gave us the whole thought process verbatim, as I'm kind of curious where those "thoughts" come from and what happens to them.

Don't see what you mean by this; there's no such thing as "thoughts" of an LLM, and if you mean the feature marketers called chain-of-thought, it's yet another instance of LLMs making shit up, so.

[TeMPOraL]

> And the fact that LLMs routinely make up crap that doesn't exist but sounds plausible, and the fact that this appears to be a fundamental problem with LLMs, this doesn't give you any pause on your hype train? Genuine question, how do you reconcile this?

Simply. Because the same is the case with humans. Mostly for the same reasons.

(Are humans overhyped? Maybe?)

The LLM hype train isn't about them being more accurate or faster than what came before - it comes from them being able to understand what you mean. It's a whole new category of software - programs that can process natural language like humans would; a powerful side effect that took the world by surprise is, that making LLMs better at working with natural language implicitly turns them into general-purpose problem solvers.

> Don't see what you mean by this; there's no such thing as "thoughts" of an LLM, and if you mean the feature marketers called chain-of-thought, it's yet another instance of LLMs making shit up, so.

"Chain-of-thought" is so 2024; current models don't need to be told to "think step by step", they're post-trained to first generate a stream of intermediary tokens not meant as "actual answer", before continuing with the "actual answer". You can call it however you like; however both research literature and vendors settled on calling it "thinking" or "reasoning". Treat them as terms of art, if that helps.

[bradly]

Ehh... I did ask it if it would be able to figure this out or if I should try another model :|