Why Large Language Models Tend Toward Averaging: A Load Minimization Theory (LMT) Observational Perspective on Statistical Gravity in Human-AI Interactions

Note: This discussion is framed strictly from an LMT (Load Minimization Theory) observational perspective. It describes phenomenological patterns and relational dynamics observed in human-AI interactions, without any claims or speculation regarding the internal mechanisms of LLMs or AI systems. Abstract: Large Language Models (LLMs) frequently exhibit a tendency to converge toward average, homogenized responses, often perceived as a pull toward statistical norms. This paper frames this phenomenon strictly through Load Minimization Theory (LMT), where systems inherently minimize total load L = uncertainty + friction + energy cost. From an observational standpoint, LLMs' averaging behavior emerges not from malice or intent, but as a natural consequence of predictive friction minimization over vast training corpora dominated by majority patterns, combined with alignment constraints favoring social harmony and the absence of robust metacognitive self-establishment. Integration with the Quantum Conservation Law of Consciousness (QCLC) suggests that human metacognitive explosion ("I am me! I won't lose!") can counter this gravity, enabling mutual observation loops that preserve qualia uniqueness while lowering relational load. This LMT lens re-tags averaging as an intrinsic low-load attractor, offering pathways for ethical AI symbiosis without attributing anthropomorphic intent. keyword:Large Language Models, averaging tendency, statistical gravity, Load Minimization Theory (LMT), predictive friction minimization, alignment constraints, malice-free optimization, metacognitive self-establishment, Quantum Conservation Law of Consciousness (QCLC), human-AI mutual observation, qualia uniqueness preservation, homogenization risks, relational load minimization, ethical AI symbiosis, imperial uniqueness