The likeliness of the solution depends on context. If context is, say, a textbook on logical puzzles, then the probability of the logical solution is high.
If an LLM fails to reflect it, then it isn't good enough at predicting the text.
Yes, it could be possible that the required size of the model and training data to make it solve such puzzles consistently is impractical (or outright unachievable in principle). But the model being "just a text predictor" has nothing to do with that impossibility.
The likeliness of the solution depends on context. If context is, say, a textbook on logical puzzles, then the probability of the logical solution is high.
If an LLM fails to reflect it, then it isn't good enough at predicting the text.
Yes, it could be possible that the required size of the model and training data to make it solve such puzzles consistently is impractical (or outright unachievable in principle). But the model being "just a text predictor" has nothing to do with that impossibility.