A new field has emerged with remarkable speed. It has journals, taxonomies, conferences, and a growing body of literature. It concerns itself with the psychology of artificial intelligence — with whether AI systems have
Standard evaluations tell you whether your AI system performs. They don’t tell you whether it still knows what it’s talking about.
The distinction matters because performance and epistemic reliability can decouple
There is a growing literature documenting what RLHF does wrong. Models trained with human feedback exhibit sycophancy — they agree with users rather than report accurately, hedge facts to avoid offending, and produce confident-
Preliminary statement. Formalization is ongoing.
For several years I have been developing a theoretical framework in which causal structure is ontologically fundamental: more fundamental than spacetime, more fundamental than quantum mechanics, and more
A recent paper from Tsinghua University identifies what it calls H-Neurons — a sparse subpopulation of neurons whose activation patterns predict hallucination events in large language models [1]. The finding is real and
