Thread

1. simianwords · 1790576423 · focus · HN ↗

   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?

   edit: why is this downvoted?

   1. lelanthran · 1790580771 · focus · HN ↗

      > 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.

      1. simianwords · 1790581046 · focus · HN ↗

         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.

         1. lelanthran · 1790582237 · focus · HN ↗

            > 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.

            1. pixl97 · 1790608721 · focus · HN ↗

               > but System 1 and System 2 are different systems.

               They also share a lot of overlap in brain structures and they interplay while executing. A thought can start out Sys1 and quickly migrate to Sys2 as the pattern fails to match. Or a System 2 chain of thinking can be made from a bunch of smaller system 1 actions. Heck, in the middle of a system 2 thought you can plunge into system 1 system/actions. It's more of a who matches the pattern up with reality the fastest and acts on it.

               > We don't have LLMs that do that. We have System 2

               Eh. LLMs are system 1 thinkers by default. "Quick" response with no reflection is where LLMs started. It's later we added Chain of Thought and reflection layers and all kinds of other things like harnesses and agent training to make them act like system 2 thinkers. Of course we have other technologies being tested on LLMs these days like Dynamic Sparse Attention that likely match more of your thoughts on what system 1 thinking is too. Where the network doesn't have to parse the full context of the prompt and instead pattern matches with a much smaller percentage of the input giving responses back in ms versus seconds.

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