> This has already been solved by GPT 5 Adaptive reasoning. A single model that knows when to reason or not based on a thinking parameter we provide (like xhigh). What’s the relevancy to post it today?

Tell me you didn't read Daniel Khaneman's book without telling me you didn't read Daniel Khaneman's book.

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simianwords 4 hours ago | parent [-]

Asking earnestly, I don’t know what you mean by this reply. I know what system 1 and 2 is. But this has already been solved using same model.

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lelanthran 4 hours ago | parent [-]

> I know what system 1 and 2 is. But this has already been solved using same model.

No, it hasn't. Maybe you have a different definition of System 1 and System 2. I last read the book well over a decade ago (2011, maybe? 2012?), but System 1 and System 2 are different systems. IOW, System 2 is not a more computational version of System 1.

The argument you made implies that System 2 is just a more capable System 1, which is not what the book (nor this paper, AIUI) proposes.

In computery terms, System 1 runs in O(1) time, System 2 runs in O(log n) (or maybe just O(n)) time.

This means that any System 1 will run the input once through the heuristics, using the same computational power and taking the same time whether the input is 100 tokens or 1 million tokens, for quick but perhaps wrong decision (not "answer"). We don't have LLMs that do that. We have System 2 - run in O(log n) time and produce an answer.

System 1 is completely bereft of thought.

	▲	simianwords 4 hours ago \| parent [-]
		> The argument you made implies that System 2 is just a more capable System 1, which is not what the book (nor this paper, AIUI) proposes. No, system 2 is the emergent capability to reason and increase the space of places to find the answer. Forget the paper's proposal, and look at the problem it is trying to solve. Ability to give quick answers, ability to give thought out answers, and the ability to know when to choose what. Adaptive reasoning does all three. > This means that any System 1 will run the input once through the heuristics, using the same computational power and taking the same time whether the input is 100 tokens or 1 million tokens, for quick but perhaps wrong decision (not "answer"). No, I don't think we humans use o(1) to for understanding 1000 tokens or 2 tokens. I simply don't think that's the case. There's a new model called "Jev" and it is literally named System 1 (from the book) and even it is billed per input token.