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  • Oct 4, 2026, 12:27 AM

    LLMs and automated systems, however, can only see the corpus of things that exist, and ways to connect them that already exist.

    To paraphrase the classic MaricSteinian axiom** - we need new approaches to solve new problems.

    LLMs are not able to do that. Statistically recombining snippets of analysis isn't novelty. It *approximates* the process that we *have done* in research.

    That *have done* is important.

    ** I made that up, it's not how the axiom is normally described

    /3

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Replies

  • Oct 4, 2026, 12:33 AM

    When we do a Research™️ - yes, understanding what we *have done* is necessary.

    And then we break it. Or abandon it. Or do something else.

    We don't grow knowledge by doing the same thing a lot of times.

    [but isn't this the scientific method? repeat lots? Pay me a billion dollars to address that one it is soooo booooring. Or read the next sentence and *understand it*].

    Repetition is how we confirm things. Not how we grow new knowledge.

    ...which is the job of academia and research.

    Right?

    /4

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  • Oct 4, 2026, 12:36 AM

    ...in that framework, how can an LLM - a statistical reassembler of things we already know about - evaluate a research output?

    You know the answer already: it cannot.

    Ipso (w00t science word!), an academia using these systems is lost. And must be abandoned - recast into something new.

    /fin

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  • Oct 4, 2026, 12:36 AM

    ps: everyone should congratulate me now for keeping that tight, there are *so many* side rambles to that one....

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  • Oct 4, 2026, 12:41 AM

    @adamsteer as a journal editor, I can't express enough the joy I feel when I deal with a review that has spelling and grammar errors and is a bit chaotic and actually thoughtful. Human reviews are close to becoming the minority.

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