Does anyone here have access to Grok 4 yet? If so, could you please try asking it to solve this basic word search problem [0] and share the results? It's just a simple grid of letters where you have to find the position of each word, the kind of problem that any young child can easily solve.
They said they're training a new base model for better multimodal performance soon. I wouldn't expect it to be able to read an image like that today. Maybe if you provided it in text format.
As a point of interest and for comparison, Gemini 2.5 Pro is able to generate a Python program that outputs the complete correct solution when run, but it can't figure out how to one-shot the problem if asked directly.
This is just a for-fun test to get a sense of how models are progressing; it highlights the jagged nature of their intelligence and capabilities. None of the big AI labs are testing for such a basic problem type, which makes it a bit of an interesting check.
I think it's still interesting to see how Grok 4 performs, even if we don't use this test to draw any broader conclusions about what capabilities it offers.
> Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not exposed, reasoning cannot be disabled, and the reasoning effort cannot be specified.
unfortunately no requests are passing because of some rate limits
These models operate on tokens, not characters. It’s true that training budgets could be spent on exhaustively enumerating how many of each letter are in every word in every language, but it’s just not useful enough to be worth it.
It’s more like asking a human for the Fourier components of how they pronounce “strawberry”. I mean the audio waves are right there, why don’t you know?
Although a vast majority of tokens are 4+ characters, you’re seriously saying that each individual character of the English alphabet didn’t make the cut? What about 0-9?
Each character made the cut, but the word "strawberry" is a single token, and that single token is what the model gets as input. When humans read some text, they can see each individual character in the word "strawberry" everytime they see that word. LLMs don't see individual characters when they process input text containing the word "strawberry". They can only learn the spelling if some text explicitly maps "strawberry" to the sequence of characters s t r a w b e r r y. My guess is there are not enough of such mappings present in the training dataset for the model to learn it well.
The fact the word ends up being 1 token doesn’t mean model can’t track individual characters in it. The model transforms token into vector (of multiple thousands dimensionality), and I’m pretty sure there are dimensions corresponding to things like “if 1st character an ‘a’, 1st is ‘b’, 2nd is ‘a’ etc.
Character on 1st/2nd/3rd place is part of semantic space in generic meaning of the word. I ran experiments which seemingly ~support my hypothesis below.
I got 0.863 (for 1st)/0.559 (for 2nd)/0.447 (for 3rd) accuracy for Qwen 3 8B model embeddings. Note the code is hacky and might be wrong in ways + in reality transformers do know more because here I utilize only embedding layer.
However it does show there are very clear signals on characters in tokens in embedding vectors.
Thank you! I guess if there's enough spelling related text in the dataset, a model is forced to learn some info about token composition in order to predict such texts.
I wonder if it would help to explicitly insert this info into an embedding vector, similar to how we encode word position info. For example, allocate the first 20 vector elements to represent ASCII codes of token's characters (in some normalized way).
I got 3 tokens: st, raw, and berry. My point still stands: processing "berry" as a single token does not allow the model to learn its spelling directly, the way human readers do. It still has to rely on an explicit mapping of the word "berry" to b e r r y explained in some text in the training dataset. If that explanation is not present in the training data, it cannot learn the spelling - in principle.
Exactly. If “st” is 123, “raw” is 456, “berry” is 789, and “r” is 17… it makes little sense to ask the models to count the [17]’s in [123,466,789]: it demands an awareness of the abstraction that does not exist.
To the extent the knowledge is there it’s from data in the input corpus, not direct examination of the text or tokens in the prompt.
[0] https://imgur.com/VxNP5jG