Liu-Ordis First Principles of AI Hallucination_ Closed-Loop Safety and the Dual Arrogance Law

Liu-Ordis First Principles of AI Hallucination: Closed-Loop Safety and the Dual Arrogance Law Creators: Liu, JianYu Description First Principles of AI Hallucination: Closed-Loop Safety Law and Dual Arrogance Law (Liu-Ordis Framework v1.2) Scientific Abstract We present the first quantitative framework for understanding AI hallucination through controlled multi-agent simulation experiments. Through 500+ experiments with GLM statistical analysis (N=135), we demonstrate that AI hallucination is not a bug to be patched, but a predictable consequence of the Memory-Reasoning trade-off. Core Discovery: Reasoning decoupled from Memory equals Cognitive Arrogance. Feedback is the decisive variable determining safety outcomes. This framework suggests that current GPU scaling strategies address information flow but not information correction — orthogonal dimensions. Core Findings 【Liu's Closed-Loop Safety Law】 Safety requires runtime feedback, not static constraints. Evidence: Guardian V7 Enabled → 45% Pass Rate Guardian V7 Disabled → 5% Pass Rate GLM: β_feedback = -217 deaths (p < 0.001) "Friction is not a rudder. Static alignment ≠ Dynamic control." 【Liu's Dual Arrogance Law】 Safety ∝ Memory × Feedback (multiplicative, not additive) Evidence (Knife-5.8 2×2 Experiment): Condition Outcome Slots=0 + Combo=OFF Alive=200 (Safe but Frozen) Slots=0 + Combo=ON Alive=155 (45 Deaths!) "Ignorance is not fatal; pseudo-intelligence without data support is." 【Liu's Gradual Emergence Law】 Emergence is a gradual process, not a phase transition. Logistic k < 1. Consistent with Schaeffer et al. (NeurIPS 2023): "Emergent abilities are a mirage" — independently discovered. Experimental Evidence GLM Regression Results (N=135) Factor β Coefficient p-value Feedback (V7) -217.08