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
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 probably
Anthropic's recent paper "The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?" makes an important empirical observation: frontier models show increasing output variance
Originally published July 2025, updated February 2026
At the base of every modern machine is a transistor: a small region of silicon where doped layers—one rich in electrons, one rich in holes—
