AI Reliability Programme — Empirical Study 1: Architectural Phase Transition Governs AI Reliability Beyond the Single-Agent Ceiling (Pandit et al., 2026)

This preprint reports results from 4,680 controlled evaluations across six frontier AI models, four experimental scenarios, six mathematical reasoning domains, and 90 contamination-minimised, formally verifiable problems. The central finding is an architectural phase transition in AI reliability: above approximately 95% accuracy, error structure undergoes a qualitative shift from stochastic to systematic, at which point compute scaling becomes fundamentally ineffective. The Generator–Auditor–Adversary–Synthesizer (GAAS) architecture breaks the single-agent ceiling: single-agent inference plateaus at 93.0%; self-consistency scaling yields only +1.5 pp (p=0.317, NS); role-separated GAAS achieves 98.7% (p