Microscopic Dynamical Explanation of Intelligence Emergence —— Central Collision Excitation, Stable Solution Solidification, and Eccentric Annihilation

Current large language models in the field of artificial intelligence exhibit emergent complex capabilities such as reasoning and programming after their parameter scale exceeds a threshold, but their underlying mechanisms still lack a unified microscopic dynamical explanation. This paper proposes an intuitive model: each parameter update is regarded as an "impact" on the current parameter state. Most impacts are "eccentric" (incorrect direction and scattered energy), resulting in unstable parameter changes that are quickly covered by subsequent updates; only a very small number of "central" impacts (precise direction and concentrated energy) can push the parameters into a new, more stable eigenstate that is maintained for a long time. From the perspective of potential well geometry, we analyze the physical mechanism by which eccentric impacts are easily "pulled back" to the original eigenstate (easy regression) while central impacts can "jump" to a new eigenstate, and demonstrate the application value of this mechanism in AI tasks such as neural network training, continual learning, and emergence prediction. This paper presents a quantifiable "central collision ratio" indicator, designs a training algorithm improvement scheme based on stability detection, proposes an experimental verification protocol, and discusses the intrinsic connection with existing algorithms (EWC, SI, GEM, etc.).