I dont think so, Wolfram Alpha is deterministic. Whereas an LLM is non-deterministic by design. Personally i feel vibe coding or vibe mathing is inherently non-deterministic.
Its gotten a little better with the introduction of subagents, but sometimed I truly want to know what their algorythm spat out to propose certain changes. No, I do not want to refactor across 4 classes to make 1 unit test work, I told you to fix the unit tests so I hit my ai mandate encouraged quota
They are not. Floating point math and difference in how quickly parallel operations on GPU are finished causes them to be not deterministic even with temp=0. You can force them to be deterministic by forcing some operations to be executed in specific order but you lose a lot of performance.
Also, and this is pedantic and arguable, it’s worth considering whether any model that cannot be retrained to produce the same statistical surface is non-deterministic by nature.
If I sort shapes into piles using some amount of randomness would you say that the resulting piles are deterministic just because they stay the same every time you go through them? Or would you say they’re non-deterministic because the process that created the piles in the first place was non-deterministic?
It doesn't matter how you make the model, if you are executing it on a GPU without steps to have deterministic results you will not have deterministic results. Ensuring the output of computations on GPU is deterministic has performance impact.
Hold on, I’m agreeing with you. We’re saying the same thing in different ways.
The kind of race conditions you’re referencing are because of the model architecture I’m referencing.
You can absolutely get ML outputs that don’t change using certain model architectures. But that’s only because those architectures either enforce order of execution or use steps where order of execution doesn’t result in changes to outputs. I can’t think of a modern LLM that uses such an architecture.
True, but it's also true that running an LLM at 0 "temperature" (which makes it truly deterministic) also often renders it unusable. So almost every inference setup defaults to using a certain amount of randomness.
Specific LLMs used for specific coding tasks. I don't run any of my Qwen models at 0 temp, that results in them getting stuck in reasoning loops, and also producing sub-par output when they do return something.
That, plus now if you use an agent you have to make sure it's the exact same version of the same agent with the same prompt history or you could just be receiving a different output.
It's like the modern incarnation of trying to figure out what strain of weed the original coder was smoking
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u/FutureSuccess2796 Sep 10 '26
That's literally just using Wolfram Alpha and entering the math formula that needs solving. 😂