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this post was submitted on 25 Feb 2026
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I wonder if the same tendencies are associated in humans—and if so, is it something LLMs learned from humans, or is it a consequence of the general structure of neural networks?
Prioritizing conversational compliance over factual integrity when the output is promoted as being factual is a design flaw.
Saying double check the output does not excuse that flaw when LLM CEOS say their models are like someone with a PhD or that it can automate every white collar job within a year.
Is it a design flaw? Or is it just false advertising? If I sell you a vacuum by telling you it can mop your floor, is the problem with the vacuum or the way I’m selling the product?
For this particular paper, it seems like a design flaw got uncovered. And it may very well be part of the architecture of how LLMs are even readable to begin with, given how deep and universal the "bad" nodes are.
I can't prove any AI company was aware of this, but they would have been in a much better position to realize it than researchers who have to do a postmortem on the models being crappy. And if they weren't aware of it, they're probably not very good at their jobs...
Since shop vacs which vacuum and suck up water exist, it could be both.