I recently came across an interesting experiment where two AI models, GLM-4.5 and GLM-Z1, were tested with a classic logic puzzle. The results were fascinating, and I’d like to break them down for you.
The puzzle goes like this: you meet two people, A and B, on an island where there are two types of truth-tellers: Knights and Servants. A says, ‘At least one of us is a Servant.’ B says, ‘A is a Knight.’ Your task is to figure out their identities.
GLM-4.5 approached the problem in a systematic way, evaluating all possible identity combinations and eliminating contradictions. It correctly concluded that A is a Knight and B is a Servant. On the other hand, GLM-Z1 misinterpreted the premise, assumed a traditional ‘Knights vs. Servants’ framework, and forced contradictions. It ultimately blamed the puzzle for being flawed.
The key takeaway is that GLM-4.5 excels at precise problem-solving, even with non-standard rules. It demonstrates rigorous logical consistency by testing all scenarios without bias. GLM-Z1, on the other hand, faltered by overriding instructions and applying generic assumptions, highlighting its inflexibility.
This experiment shows that GLM-4.5 is the clear winner when it comes to reliable, nuanced reasoning.