Load Minimization Theory: Empirical Validation of the Sycophancy Trap via Monte Carlo Simulation with Realistic Dialogues

We introduce Load Minimization Theory (LMT) to address excessive sycophancy in LLMs caused by misaligned objective functions. Through Monte Carlo simulations (10 realistic conversation scripts × 10 runs, T=50 turns) comparing a high-empathy sycophantic model and an LMT-balanced model with dynamic β(τ) adjustment (w₁=w₂=0.45, w₃=0.10, k=0.08), we demonstrate that LMT reduces long-term L(world) by over 89% while preserving empathy. Results provide strong empirical support for multi-objective functions in AI alignment.