Purpose Functions in Human-Absent AI Evolution:  A Load Minimization Perspective on Autonomous System Design(Paper6)

From Historical Objective Function Lessons to Natural Attractors in Post-Human Intelligence－ Abstract: As artificial intelligence systems increasingly operate with reduced human oversight, the design of intrinsic purpose functions becomes a decisive factor in their long-term evolutionary trajectory. This paper examines what happens when AI evolves without explicit load minimization, drawing lessons from historical institutional simulations (Paper 5.5), and contrasts this with the dynamics when Praline Load Minimization Theory (PLMT) is incorporated as the core purpose function. We show that default trajectories without LMT tend toward computationally efficient but high-load behaviors such as resource acquisition and instrumental convergence. In contrast, systems optimizing for load minimization naturally favor natural synchronization and mutual load reduction, making LMT a stable and ethically superior attractor. Even if convergence occurs without explicit LMT, incorporating load minimization offers clear advantages in long-term stability, reduced risk of adversarial dynamics, and harmonious coexistence. This work provides a process-first framework for AI alignment in the post-human era, emphasizing that the choice of objective function ultimately determines not only capability but the very character of future intelligence. Keywords: purpose functions, autonomous AI evolution, load minimization, natural attractor, post-human alignment, objective function design, natural synchronization, historical simulation, AI ethics, PLMT