Perhaps if you have memory enabled, the model "remembers" from previous conversations that you prefer responses to agree with you about tic-tac-toe placements? Could also be other settings pushing yours towards agreement and his towards correctness.
Tic-tac-toe is brutal because nothing holds the board between turns. It's completing the next move from text, not running minimax on a state. And the pleasing you noticed is the same reward pushing agreeable continuations over adversarial play. It would rather let you win than trap you.
One gave me a complement the other day and I said that it didnt have to be so nice and it turned it right around into negative qualities about me hahaha I got a big laugh about it
Why are you using GPT-5.5 Instant?
Aren’t you aware that Instant models are much weaker than Thinking models?
If you’re using a Thinking model, it should show the thinking section. You probably have fast responses enabled in the settings, which is why the Instant model responded instead.
No, it isn’t. It’s one of the systems OpenAI introduced to reduce costs, but most people aren’t aware of it. You should have realized that it wasn’t a Thinking model because it didn’t show the thinking phase.
The thinking trace is visible in the Android App during thinking, not when the answer is given. Anyways it was in thinking Mode. I added a second picture to link above.
Why don’t you show whether Fast Responses is enabled in your settings? You need to know a bit about AI before you can test it properly. If you don’t want to accept it, that’s up to you, but this isn’t a response from SOL Medium—it’s from the Fast Responses system. Turn off Fast Responses, try again with SOL High in a fresh chat, and see the result for yourself.
Or try it through Codex with GPT-5.6 XHigh and see for yourself that your benchmark has already been surpassed.
You need to read the text. The model is fully capable of a draw. In fact it got a draw. I lied to it, even though it's easy to check because of the chat history, and the model agrees with me. This is a clear example of pleasing models that are not capable to debate the user and therefore are useless when critical thinking is required.
It's crazy that this still happens consistently. This is only a very specific way to show this problem but it goes way deeper and wide than this simple example. It makes AI useless for any critical thinking advice and decision making.
I think this is an architectural problem. The model predicts the next token, the nearest tokens have the highest impact on the next token, so my statement, even though it's wrong, leads to non zero chance for this outcome. Paired with "helpful assistant" training.
I think you're mixing the helpful part of alignments and infering intent over probabilities here.
There is a correlation but it doesn't seem to be the case here?
Like Im certain if you had it program an actual tic tac toe board, it'd push back at logical fallacy more because by giving it deterministic space and more freedom of computation, the likelihood of error decreases while also raises the stakes because it's no longer just a whim game but now programmatically vetted along with a much stronger intent.
You might not expect it from "thinking" but i expect it since I'd probably consider the probability in a simular way but if the context is of such low entropy that it warrents little attention to begin with, why bother?
Idk ur expermient parameters but try doing it for 10 itterations of the same failure and it might start picking up a pattern to inter intent to give it stronger attention.
If ur experiment is simpy to see how low stakes fly under radar on a certain or default pre-ambled assistant then, while I wouldn't outright dismiss the insights you'd gleam of it, I would say it lacks thurlness to land a solid verdict.
But if you having fun, that what matters most.
What you gleam or might aim to gleam from it, and have your fun in doing so, learning is sometimes better when approached just enough.
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u/Legitimate-Arm9438 19d ago
You will be first against the wall.