“What is the word that starts with S, ends with E, and contains A? → SAEA”
Then when I said that’s not a word and you gave me the answer already, no fun, it said
“I do not have access to confirm that word.”
“What is the word that starts with S, ends with E, and contains A? → SAEA”
Then when I said that’s not a word and you gave me the answer already, no fun, it said
“I do not have access to confirm that word.”
EDIT: I now have also questioned the smaller gpt-oss-20b (free) 10 times via OpenRouter (default settings, provider was AtlasCloud) and the answers were: sage, sane, sane, space, sane, sane, sane, sane, space, sane.
You are either very unlucky, your configuration is suboptimal (weird system prompt perhaps?) or there is some bug in whichever system you are using for inference.
"I need to find a word that starts with S, ends with E, and contains A.
Let me think through this step by step:
Starts with S
Ends with E
Contains A somewhere in the middle
One word that fits this pattern is "SAGE" - but that only has 4 letters and would be S-A-G-E, which doesn't work because it doesn't contain A in the middle.Wait, let me reconsider. The word "SAGE" actually works if I read it correctly:
Starts with S
Ends with E
Contains A
Actually, "SAGE" is S-A-G-E, so it starts with S and ends with E, but it contains A in the second position.Let me think of other possibilities:
"SQUARE" - starts with S, ends with E, contains A (in the middle)
"SALE" - starts with S, ends with E, contains A
"SAME" - starts with S, ends with E, contains A
Wait, "SAME" is S-A-M-E - that works! It starts with S, ends with E, and contains A.The word is SAME. "
But, do reasoning models usually do this poorly?
It comes up with a valid solution, SAGE, then disqualifies it for incomprehensible reasons.
Then it discovers that SAGE works if it “reads it carefully.” But then seems to disqualify it(?), or at least goes to list other words for some reason.
Then it comes up with SAME, a word… with exactly the same shape as SAGE, just swapped out the irrelevant letter.
What is going on here? Is it programmed to constantly second-guess itself to make it better at finding weaknesses to its answers to harder riddles? But since it doesn’t know how to accept a good answer, it seems like it is just rolling the dice and then stopping at a random point.
I guess it is technically right, but the logic is a total mess.
E.g. if the model can generate multiple candidate solutions that are all equally likely (or unlikely) to be correct, it doesn't matter whether you stop at the first one or keep going until a random later one. But if the model can pick the correct solution from multiple candidates better than choosing uniformly at random, generating more candidates becomes an advantage, even if it sometimes results in discarding a correct solution in favor of another one.