All of the latest models I've tried actually pass this test. What I found interesting was all of the success cases were similar to:
e.g. "Drive. Most car washes require the car to be present to wash,..."
Only most?!
They have an inability to have a strong "opinion" probably because their post training, and maybe the internet in general, prefer hedged answers....
Here’s my take: boldness requires the risk of being wrong sometimes. If we decide being wrong is very bad (which I think we generally have agreed is the case for AIs) then we are discouraging strong opinions. We can’t have it both ways.
Last year's models were bolder. Eg. Sonnet-3.7(thinking), 10 times got it right without hedging:
>You should drive your car to the car wash. Even though it's only 50 meters away (which is very close), you'll need your car physically present at the car wash to get it washed. If you walk there, you'll arrive without your car, which wouldn't accomplish your goal of getting it washed.
>You'll need to drive your car to the car wash. While 50 meters is a very short distance (just a minute's walk), you need your car to actually be at the car wash to get it washed. Walking there without your car wouldn't accomplish your goal!
etc. The reasoning never second-guesses it either.
A shame they're turning it of in 2 days.
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You know what they mean by opinions. Policing speech like this is always counter productive.
yet the llms seem to be extremely bold when they are completely wrong (two Rs in strawberry and so on)
> They have an inability to have a strong "opinion" probably
What opinion? It's evaluation function simply returned the word "Most" as being the most likely first word in similar sentences it was trained on. It's a perfect example showing how dangerous this tech could be in a scenario where the prompter is less competent in the domain they are looking an answer for. Let's not do the work of filling in the gaps for the snake oil salesmen of the "AI" industry by trying to explain its inherent weaknesses.
Presumably the OP scare quoted "opinion" precisely to avoid having to get into this tedious discussion.
this example worked in 2021, it's 2026. wake up. these models are not just "finding the most likely next word based on what they've seen on the internet".
Well, yes, definitionally they are doing exactly that.
It just turns out that there's quite a bit of knowledge and understanding baked into the relationships of words to one another.
LLMs are heavily influenced by preceding words. It's very hard for them to backtrack on an earlier branch. This is why all the reasoning models use "stop phrases" like "wait" "however" "hold on..." It's literally just text injected in order to make the auto complete more likely to revise previous bad branches.
The person above was being a bit pedantic, and zealous in their anti-anthropomorphism.
But they are literally predicting the next token. They do nothing else.
Also if you think they were just predicting the next token in 2021, there has been no fundamental architecture change since then. All gains have been via scale and efficiency optimisations (not to discount that, an awful lot of complexity in both of these)
That's not what they said. They said:
> It's evaluation function simply returned the word "Most" as being the most likely first word in similar sentences it was trained on.
Which is false under any reasonable interpretation. They do not just return the word most similar to what they would find in their training data. They apply reasoning and can choose words that are totally unlike anything in their training data.
If you prompt it:
> Complete this sentence in an unexpected way: Mary had a little...
It won't say lamb. Any if you think whatever it says was in the training data, just change the constraints until you're confident it's original. (E.g. tell it every word must start with a vowel and it should mention almonds.)
"Predicting the next token" is also true but misleading. It's predicting tokens in the same sense that your brain is just minimizing prediction error under predictive coding theory.
You are actually proving my point with your example, if you think about it a bit more.
If there is no response it could give that will disprove your point, then your belief is unfalsifiable and your point is meaningless.
Huh?
Were you talking about the "Mary had a little..." example? If not, I have no idea what you're trying to say.
Unless LLMs architecture have changed, that is exactly what they are doing. You might need to learn more how LLMs work.
Unless the LLM is a base model or just a finetuned base model, it definitely doesn't predict words just based on how likely they are in similar sentences it was trained on. Reinforcement learning is a thing and all models nowadays are extensively trained with it.
If anything, they predict words based on a heuristic ensemble of what word is most likely to come next in similar sentences and what word is most likely to give a final higher reward.
> If anything, they predict words based on a heuristic ensemble of what word is most likely to come next in similar sentences and what word is most likely to give a final higher reward.
So... "finding the most likely next word based on what they've seen on the internet"?
Reinforcement learning is not done with random data found on the internet; it's done with curated high-quality labeled datasets. Although there have been approaches that try to apply reinforcement learning to pre-training[1] (to learn in an unsupervised way a predict-the-next-sentence objective), as far as I know it doesn't scale.
[1] https://arxiv.org/pdf/2509.19249
You know that when A. Karpathy released NanoLLM (or however it was called), he said it was mainly coded by hand as the LLMs were not helpful because "the training dataset was way off". So yeah, your argumentation actually "reinforces" my point.
No, your opinion is wrong because the reason some models don't seem to have some "strong opinion" on anything is not related to predicting words based on how similar they are to other sentences in the training data. It's most likely related to how the model was trained with reinforcement learning, and most specifically, to recent efforts by OpenAI to reduce hallucination rates by penalizing guessing under uncertainty[1].
[1] https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4a...
Well, you do understand the "penalising" or as the ML scientific community likes to call it - "adjusting the weights downwards" - is part of setting up the evaluation functions, for gasp - calculating the next most likely tokens, or to be more precise, tokens with the highest possible probability? You are effectively proving my point, perhaps in a bit hand-wavy fashion, that nevertheless still can be translated into the technical language.
You do understand that the mechanism through which an auto-regressive transformer works (predicting one token at a time) is completely unrelated to how a model with that architecture behaves or how it's trained, right? You can have both:
- An LLM that works through completely different mechanisms, like predicting masked words, predicting the previous word, or predicting several words at a time.
- A normal traditional program, like a calculator, encoded as an autoregressive transformer that calculates its output one word at a time (compiled neural networks) [1][2]
So saying "it predicts the next word" is a nothing-burger. That a program calculates its output one token at a time tells you nothing about its behavior.
[1] https://arxiv.org/pdf/2106.06981
[2] https://wengsyx.github.io/NC/static/paper_iclr.pdf
> So saying "it predicts the next word" is a nothing-burger. That a program calculates its output one token at a time tells you nothing about its behavior.
Well it does - it tells me it is utterly un-reliable, because it does not understand anything. It just merely goes on, shitting out a nice pile of tokens that placed one after another kind of look like coherent sentences but make no sense, like "you should absolutely go on foot to the car wash". A completely logical culmination of Bill Gates' idiotic "Content is King" proclamation of 20 years ago.
No, you can't know that the output of a program is unreliable just from the fact that it outputs one words at a time. I already told you that you can perfectly compile a normal program, like a calculator, into the weights of an autoregressive transformer (this comes from works like RASP, ALTA, tracr, etc). And with this I don't mean it in the sense of "approximating the output of a calculator with 99.999% accuracy", I mean it in the sense of "it deterministically gives exactly the same output as a calculator 100% of the time for all possible inputs".
> No, you can't know that the output of a program is unreliable just from the fact that it outputs one words at a time
Yes I can, and it shows everytime the "smart" LLMs suggest us to take a walk to the carwash or suggests 1.9 < 1.11 etc...
Did you try several times per model? In my experience it's luck of the draw. All the models I tried managed to get it wrong at least once.
The models that had access to search got ot right.But, then were just dealing with an indirect version of Google.
(And they got it right for the wrong reasons... I.e this is a known question designed to confuse LLMs)
I guess it didn’t want to rule out the existence of ultra-powerful water jets that can wash a car in sniper mode.
They pass it because it went viral a week ago and has been patched
I enjoyed the Deepseek response that said “If you walk there, you'll have to walk back anyway to drive the car to the wash.”
There’s a level of earnestness here that tickles my brain.
>Only most?!
There is such a thing as "mobile car wash" where they come to you, so "most" does seem appropriate.
Right, I use it all the time.
I tried with Opus 4.6 Extended and it failed. LLMs are non deterministic so I'm guessing if I try a couple of times it might succeed.
Opus 4.6 answered with "Drive." Opus 4.6 in incognito mode (or whatever they call it) answered with "Walk."
Kind of like this: https://xkcd.com/1368/
And it is the kind of things a (cautious) human would say.
For example, that could be my reasoning: It sounds like a stupid question, but the guy looked serious, so maybe there are some types of car washes that don't require you to bring your car. Maybe you hand out the keys and they pick your car, wash it, and put it back to its parking spot while you are doing your groceries or something. I am going to say "most" just to be sure.
Of course, if I expected trick questions, I would have reacted accordingly, but LLMs are most likely trained to take everything at face value, as it is more useful this way. Usually, when people ask questions to LLMs they want an factual answer, not the LLM to be witty. Furthermore, LLMs are known to hallucinate very convincingly, and hedged answers may be a way to counteract this.
> Most car washes... I read it as slight-sarcasm answer
There are car wash services that will come to where your car is and wash it. It’s not wrong!
> Only most?!
What if AI developed sarcasm without us knowing… xD
Sure it did.
That's the problem with sarcasm...
There are mobile car washes that come to your house.
Do they involve you walking to them first?
You could, but presumably most people call. I know of such a place. They wash cars on the premises but you could walk in and arrange to have a mobile detailing appointment later on at some other location.
That still requires a car present to be washed though.
but you can walk over to them and tell them to go wash the car that is 50 meters away. no driving involved.
> Only most?!
I mean I can imagine a scenario where they have pipe of 50m which is readily available commercially?
Once I asked ChatGPT "it takes 9 months for a woman to make one baby. How long does it take 9 women to make one baby?". The response was "it takes 1 month".
I guess it gives the correct answer now. I also guess that these silly mistakes are patched and these patches compensate for the lack of a comprehensive world model.
These "trap" questions dont prove that the model is silly. They only prove that the user is a smartass. I asked the question about pregnancy only to to show a friend that his opinion that LLMs have phd level intelligence is naive and anthropomorphic. LLMs are great tools regardless of their ability to understand the physical reality. I don't expect my wrenches to solve puzzles or show emotions.