[TeMPOraL]

> LLMs apologize and then proudly present the exact same output as before, repeatedly, forever spinning their wheels at the first major obstacle to their reasoning.

So basically like a human, at least up to young adult years in teaching context[0], where the student is subject to authority of the teacher (parent, tutor, schoolteacher) and can't easily weasel out of the entire exercise. Yes, even young adults will get stuck in a loop, presenting "the exact same output as before, repeatedly, forever spinning their wheels at the first major obstacle to their reasoning", or at least until something clicks, or they give up in shame (or the teacher does).

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[0] - Which is where I saw this first-hand.

[jdiff]

As someone currently engaged in teaching the Adobe suite to high school students, that doesn't track with what I see. When my students are getting stuck and frustrated, I look at the problem, remind them of the constraints and assumptions the software operates under. Almost always they realize the problem without me spelling it out, and they reinforce the mental model of the software they're building. Often noticing me lurking and about to offer help is enough for them to pause, re-evaluate, and catch the error in their thinking before I can get out a full sentence.

Reminding LLMs of the constraints they're bumping into doesn't help. They haven't forgotten, after all. The best performance I got out of the LLMs in my project I mentioned upthread was a loop of trying out different functions, pausing, re-evaluating, realizing in its chain of thought that it didn't fit the constraints, and trying out a slightly different way of phrasing the exact same approach. Humans will stop slamming their head into a wall eventually. I sat there watching Gemini 2.5 Pro internally spew out maybe 10 variations of the same function before I pulled the tokens it was chewing on out of its mouth.

Yes, sometimes students get frustrated and bail, but they have the capacity to learn and try something new. If you fall into an area that's adjacent to but decidedly not in their training data, the LLMs will feel that pull from the training data too strongly and fall right into that rut, forgetting where they're at.