Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.
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I love to show that kind of shit to AI boosters. (In case you're wondering, the numbers were chosen randomly and the answer is incorrect).
They go waaa waaa its not a calculator, and then I can point out that it got the leading 6 digits and the last digit correct, which is a lot better than it did on the "softer" parts of the test.
if you’re considering pasting the output of an LLM into this thread in order to fail to make a point: reconsider
lmao: they have fixed this issue, it seems to always run python now. Got to love how they just put this shit in production as "stable" Gemini 2.5 pro with that idiotic multiplication thing that everyone knows about, and expect what? to Eliza Effect people into marrying Gemini 2.5 pro?
Oh and also for the benefit of our AI fanboys who can't understand why we would expect something as mundane from this upcoming super-intelligence, as doing math, here's why:
Have they fixed it as in genuinely uses python completely reliably or "fixed" it, like they tweaked the prompt and now it use python 95% of the time instead of 50/50? I'm betting on the later.
Yeah, I'd also bet on the latter. They also added a fold-out button that shows you the code it wrote (folded by default), but you got to unfold it or notice that it is absent.
Non-deterministic LLMs will always have randomness in their output. Best they can hope for is layers of sanity checke slowing things down and costing more.
If you wire the LLM directly into a proof-checker (like with AlphaGeometry) or evaluation function (like with AlphaEvolve) and the raw LLM outputs aren't allowed to do anything on their own, you can get reliability. So you can hope for better, it just requires a narrow domain and a much more thorough approach than slapping some extra firm instructions in an unholy blend of markup languages in the prompt.
In this case, solving math problems is actually something Google search could previously do (before dumping AI into it) and Wolfram Alpha can do, so it really seems like Google should be able to offer a product that does math problems right. Of course, this solution would probably involve bypassing the LLM altogether through preprocessing and post processing.
Also, btw, LLM can be (technically speaking) deterministic if the heat is set all the way down, its just that this doesn't actually improve their performance at math or anything else. And it would still be "random" in the sense that minor variations in the prompt or previous context can induce seemingly arbitrary changes in output.
One of the big AI companies (Anthropic with claude? Yep!) wrote a long paper that details some common LLM issues, and they get into why they do math wrong and lie about it in "reasoning" mode.
It's actually pretty interesting, because you can't say they "don't know how to do math" exactly. The stochastic mechanisms that allow it to fool people with written prose also allow it to do approximate math. That's why some digits are correct, or it gets the order of magnitude right but still does the math wrong. It's actually layering together several levels of approximation.
The "reasoning" is just entirely made up. We barely understsnd how LLMs actually work, so none of them have been trained on research about that, which means LLMs don't understand their own functioning (not that they "understand" anything strictly speaking).
Thing is, it has tool integration. Half of the time it uses python to calculate it. If it uses a tool, that means it writes a string that isn't shown to the user, which runs the tool, and tool results are appended to the stream.
What is curious is that instead of request for precision causing it to use the tool (or just any request to do math), and then presence of the tool tokens causing it to claim that a tool was used, the requests for precision cause it to claim that a tool was used, directly.
Also, all of it is highly unnatural texts, so it is either coming from fine tuning or from training data contamination.
Also, if the LLM had reasoning capabilities that even remotely resembled those of an actual human, let alone someone who would be able to replace office workers, wouldn't they use the best tool they had available for every task (especially in a case as clear-cut as this)? After all, almost all humans (even children) would automatically reach for their pocket calculators here, I assume.
Well, it did reach for "I double checked it, I'm totally sure now" language.
From the perspective of trying to convince the top brass that they are making good progress towards creating an artificial psychopath - not just an artificial human - it's pretty good.
A tool uses an LLM, the LLM uses a tool. What a beautiful ouroboros.
We barely understsnd how LLMs actually work
I would be careful how you say this. Eliezer likes to go on about giant inscrutable matrices to fearmoner, and the promptfarmers use the (supposed) mysteriousness as another avenue for crithype.
It's true reverse engineering any specific output or task takes a lot of effort and requires access to the model's internals weights and hasn't been done for most tasks, but the techniques exist for doing so. And in general there is a good high level conceptual understanding of what makes LLMs work.
which means LLMs don’t understand their own functioning (not that they “understand” anything strictly speaking).
This part is absolutely true. If you catch them in mistake, most of their data about responding is from how humans respond, or, at best fine-tuning on other LLM output and they don't have any way of checking their own internals, so the words they say in response to mistakes is just more bs unrelated to anything.
233,324,900,064.
Off by 474,720.
I find it a bit interesting that it isn't more wrong. Has it ingested large tables and got a statistical relationship between certain large factors and certain answers? Or is there something else going on?
I posted a top level comment about this also, but Anthropic has done some research on this. The section on reasoning models discusses math I believe. The short version is it has a bunch of math in its corpus so it can approximate math (kind of, seemingly, similar to how you'd do a back of the envelope calculation in your head to get the orders of magnitude right) but it can't actually do calculations which is why they often get the specifics wrong.
reasoning models
that’s a shot, everyone drink up