Comment by beloch
3 hours ago
This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words. LLM's don't "think" or "reason" in the normal definition of those terms. They can do some pretty amazing things, but still screw up basic things like telling you something that is obviously wrong and contradicts the top search results.
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make people accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output. Are they not being sued over this kind of thing?
LLMs still can't do math nor count letters in words. Nothing has changed there.
This is true but a sufficiently smart LLM (run in a harness like opencode, no special MCP, no customization done whatsoever) will quickly turn out a basic 1 to 2 page sized python script to do the math. They can't do the math with any guarantee of accuracy with their own internal reasoning since it's a language model.
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
Heck, just for fun I asked a reasonably smart LLM to re-implement the Karney formula (which is considerably more complex than Vincenty), just in case I ever had a need to calculate the distance between two points down to the nanometer, and it did it: https://www.google.com/search?&q=karney+formula+geodetic+
reference: https://github.com/pbrod/karney
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
This just exposes that they don't even do the thing you said.
Not only is it still true that they can't do math directly, but not even indirectly.
They didn't write a python script to do the math, they found bits of code that are associated with "math" and the supplied arguments.
Someone else already wrote that code and someone else categorized it so that it could be associated with the kinds of problems it applies to.
That isn't an example of idiot at one thing while good at another thing, or solving the same problem just a different way or indirectly. It's being the same idiot at all times. If an actual non idiot thinker didn't write code in the problem domain, and some non idiot thinker didn't tag it as being relevant to that domain, then it wouldn't happen.
It's nothing more than an sql query.
2 replies →
I wish I could not do math like LLMs
"LLMs can't do math" is a pretty hot take in September 2026.
They literally cannot. They can detect the user’s intent to do math, and then use a different tool to do math, hopefully with the correct inputs. The LLM is not suited to giving deterministic answers to math problems.
9 replies →
LLMs cannot do math. They can generate tool calls as text that allow them to drive programs and proof agents. Compare and contrast this against human brains who can do math in the same context without needing external tools. We don't need to bring a calculator to count the letters in a sentence. It is a different neural machinery.
1 reply →
That is a conflation of LLMs (which have clear limitations) and complex harnesses of which an LLM is one component.
I think it is clear that future AI may incorporate an LLM as a component but the current concept of LLMs are a transitional form that will give way to more capable composite models.
3 replies →
They can do math but not arithmetic, which I assume is what the commenter meant
6 replies →
ChatGPT live mode still hallucinates letters in words like this. HuskIRL and FatherPhi on youtube have done some hilarious videos with it in the last couple of weeks. Beyond miscounting the Rs in strawberry, ChatGPT will say there are two Ds in "your mom" and one D in "uranus" . I tried it myself to check that the videos weren't fake and sure enough it still has this failure mode.
Calling it a 'failure mode' implies it could be fixed. This is an inherent flaw in how LLMs work and will never go away until some new kind of architecture that can actually "read text" comes along.
It seems that it can be fixed by simply doing away with Byte Pair Encoding tokenization.
Byte Latent Transformer - https://arxiv.org/abs/2412.09871
1.1% vs 99.9% on a vanilla vs byte latent transformer on a CUTE Spelling benchmark. Char and Word manipulation benchmarks also saw huge gains.
Seems fairly trivially fixable to me, e.g. by allowing the LLM to call a tool to spell out a word.
1 reply →
One "fix" is for the caller to correctly classify those fundamentally impossible tasks and pass them to a subprocess.
Some future "AI" could be a billion benchmark-hacks and a way to tell which one is needed.
we already fixed it with reasoning
They're not fundamentally unsolvable - even bigger networks with even more training can simply be trained to give the correct answers to all of these questions.
> ChatGPT will say there are two Ds in "your mom" and one D in "uranus"
… Isn't it possible that it understands the innuendo and is going along with making the joke?
In between solving open math problems, the 200 IQ robot is now casually dropping bantz onto humans so hard that they don't even know what happened, and even gets them to go telling everyone else about it without realizing. Beautiful. 10/10 timeline.
How many LLM users have anything in their prompt against "going along with jokes"? I'd guess not many.
What a wonderful new world.
Why is this getting downvoted? Is it not a reasonable question? I was wondering the same thing. Both sound like jokes to me. If the LLM is trained on text, including internet comments, how is this outlandish? It seems very likely to my uneducated self that “two Ds in your mom and one in Uranus!” is a joke.
1 reply →
LLMs might not reason exactly like humans, but they do produce much better results if you turn reasoning on.
The "crack-addled idiot savant" phase was really circa 2024, before the big labs figured this out.
I think the issue here is that Google decided that doing reasoning in the AI overviews in Google search would be too slow (and probably also too expensive), so it's still stuck making 2024-era mistakes.
>Are they not being sued over this kind of thing?
Maybe but you have to have deep pockets just to get to the starting line. And then you need standing, and some injury to argue.
Corporations have been remarkably successful at arguing they are operating within the bounds of free speech, whether or not what is said is factual, and whether or not any fact checking has been done.
> This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words.
The specific issue of Google is that they are using an underpowered model, not fit to task, and much prone to hallucination than either OpenAI or Anthropic free tier offerings.
Google should at least match the frontier labs at the free tier (with some limit; after that, degrade quality), ffs
You're asking for something unreasonable. The number of Google searches per day is enormous and they haven't even been able to roll out AI overviews to everyone yet (they're missing in a new Firefox profile I just created). I wouldn't be surprised if the free tier frontier models cost over 100x more to serve than the AI overviews.
The specific issue is that search has become so bad that they think an LLM that gets answers wrong half of the time is a valid alternative, or, in fact, the "future" of search. Then they shoved that "alternative" to users with no way to disable it.
The less specific issue is that Google has no internal incentives to produce products that are useful to customers.
LLMs are fundamentally predicting the next word to make coherent text. If you've ever played with a Markov chain text generator you've done this with a fairly dumb predictor that maintains coherence over a very short distance. Deep transformer neutral networks can do it with a much longer coherence distance but they are fundamentally performing the same operation. After "Question: Did the team make the playoffs? Answer:" a reasonable completion is "yes, the team made the playoffs". An early demonstration of GPT-2 was a fake news article about scientists discovering unicorns in Antarctica - the model doesn't "know" whether or not unicorns exist in Antarctica, but it's able to complete "Breaking news! Scientists have discovered a colony of English-speaking unicorns in Antarctica." by adding "The unicorns have a developed society with running water and electricity." because that's a sensible next sentence. (I didn't look up the actual text it wrote)
It says this this at the bottom of every one of the dumb responses that Google's trash-tier bot puts above the (deliberately awful, these days) search results:
AI can make mistakes, so double-check responses
https://www.dw.com/en/german-court-holds-google-liable-for-f...