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Why I'm still bearish on LLMs after Navier-Stokes

496 points · 653 comments · jaykru

  1. carodgers · · focus · HN ↗
    This April 2026 paper is a fun and related read.

    <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;html&#x2F;2509.24239v4" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;html&#x2F;2509.24239v4

    Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for illegal moves. With illegal asks discarded, none of the bots could beat a chess model calibrated to 1100 ELO.

    The author of the originating post says that &quot;current frontier models need laborious oversight and guardrails on even the simplest tasks&quot;, and he&#x27;s absolutely correct.

    1. joefourier · · focus · HN ↗
      &gt; current frontier models

      &gt; Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1

      The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

      1. sigmoid10 · · focus · HN ↗
        The actual current frontier plays somewhere around GM level.

        <a href="https:&#x2F;&#x2F;chessbench-ai.github.io&#x2F;#leaderboard" rel="nofollow">https:&#x2F;&#x2F;chessbench-ai.github.io&#x2F;#leaderboard

        It&#x27;s also worth noting that the very latest models (GPT-6 and Fable 5.1) actually play worse than their immediate predecessors, so it is likely that the labs are not benchmaxxing for this yet. If they did, I&#x27;m sure they could come up with something superior to humans. But there is probably very little demand for this compared to IT stuff.

        1. minraws · · focus · HN ↗
          I know HN readers and posters just read numbers and can&#x27;t be bothered to read, but please read the methodology before making any claims.

          &gt; About their ELO ratings from their own website:

          &gt; A field-relative rating calculated within ChessBench. It compares performance among the tested models and is not a direct equivalent of a human chess rating.

          I am around 1600 elo in over the board I can mop up Astra Fable etc even if I give them literal infinite time and all the subagents and internet access..

          Please folks at least use your AIs to read stuff before making claims.

          AI is not GM level, it&#x27;s not even 1600, I am 1600 by using memorized openings people frequently fall for with very basic intuitions.

          A GM is 2600 they can beat me in under 20 moves...

          Why do I even scroll through this website. For a moment I truly felt fooled, but then I read like a human should.

          Maybe I should stop doing that will be a happier life, don&#x27;t think just believe in the AGI.

          1. dmurray · · focus · HN ↗
            &gt; I am around 1600 elo in over the board I can mop up Astra Fable etc even if I give them literal infinite time and all the subagents and internet access.

            I don&#x27;t believe this.

            You refer to &quot;subagents&quot;, so this is not just an LLM but an LLM with some kind of agentic harness. Any reasonable harness and prompt, given internet access and appropriately prompted to succeed on this task, is more than capable of firing up Lichess or chess.com and relaying moves back to you. The free levels will be enough to beat you.

            A frontier model can also likely one shot a chess engine that plays at your level, again if given an environment in which it can do that.

            I completely believe the LLM on its own can&#x27;t play a full game of chess at your level. Though I&#x27;d bet that with enough reinforcement learning it is possible to train a pure transformer architecture to do that. We just don&#x27;t do it because there are other approaches that play chess much better.

            1. lirolero · · focus · HN ↗

              [dead]

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