Protocol Title Validating Universal Trinity Theory: Does Explicit Identification of Rules, Constraints, and Fear Reduce Cognitive Execution Failures in Large Language Models?

This pre-registered study tests Universal Trinity Theory, which proposes that cognitive execution failures in intelligent systems occur not from lack of knowledge but from inability to maintain simultaneous awareness of all governing elements during execution. The theory identifies three universal elements: Rules (what must be done), Constraints (what limits options), and Fear (what failure modes must be prevented). Design: We employ a 2x2 factorial design crossing two factors: - Factor 1: PURE Framework (Absent vs Present) - Factor 2: Trinity Identification (Implicit vs Explicit) This creates four conditions: 1. Baseline (Framework Absent, Trinity Implicit) 2. Trinity-Only (Framework Absent, Trinity Explicit) 3. PURE-Implicit (Framework Present, Trinity Implicit) 4. PURE-Explicit (Framework Present, Trinity Explicit) Two-Phase Execution: - Phase 1 (Single-Shot): 1,800 trials measuring first-try execution quality. Each LLM receives one attempt per task with no feedback. - Phase 2 (Iterative Feedback): Trials not meeting success threshold (Trinity Composite < 0.80) receive standardized, Trinity-neutral feedback and up to 3 correction attempts. Estimated ~2,400-3,600 additional iterations. Sample: Phase 1: 1,800 trials (6 LLM platforms x 15 tasks x 4 conditions x 5 repetitions). Phase 2: ~1,200 trials entering feedback loop with up to 3 iterations each. Analysis: - Phase 1: 2x2 factorial ANOVA testing main effects of Framework and Explicit Trinity, plus their interaction. - Phase 2: Poisson/survival analysis for iterations-to-success; convergence analysis comparing final scores across conditions. Expected Outcomes: - Phase 1: Significant main effects for both factors, with PURE-Explicit showing highest first-try success. - Phase 2: PURE-Explicit requiring fewer iterations to reach success, with advantages persisting (not converging) after feedback. Significance: This study provides the first empirical test of whether prompt engineering advantages persist during iterative refinement or converge as all approaches eventually reach similar outcomes. Findings will inform both theoretical understanding of AI cognitive architecture and practical decisions about prompt engineering investment.