Researchers Identify Hard Accuracy Limits in Transformer Models, Map Impossibility Results Into Design Rules

A new thesis transforms classical impossibility results—from Turing and No Free Lunch theorems—into concrete design specifications for AI systems, proving that transformer architectures hit an accuracy ceiling determined by layer count and embedding width alone, independent of training data or methods. The "Deterministic Horizon," measurable before deployment and found to range from 19 to 31 layers across twelve architectures, establishes sixteen computable boundaries where violations impose quantified costs, from auction mechanism failures to 110–190x overhead in zero-knowledge neural verification.

Why it matters: Understanding hard architectural limits helps practitioners and researchers design trustworthy systems by clarity on what is impossible rather than spending resources chasing unachievable performance targets.