Kaining Wang

To address the well-known limitations in current artificial-intelligence behaviour research, including static modelling, rigid dimension binding, scenario-dependent evaluation, and anthropomorphic debates inherited from human personality psychology, this paper constructs a multidimensional behaviour-analysis framework built upon two orthogonal axes: a depth-of-function hierarchy axis and a temporal-persistence mode axis. We strictly establish a two-layer empirical behavioural system for artificial intelligence, consisting of the observable behavioural layer and the implicit-functional descriptive layer, while controversial consciousness-subconsciousness domains are explicitly bracketed as out-of-bound research questions to eliminate all ontological disputes. Within the observable behavioural layer, we define two orthogonal, dynamically coupled behavioural modes: the transient mode, which describes behavioural patterns that continuously evolve over the timeline of a single dialogue session and vanish once the session terminates; and the persistent mode, which denotes behavioural tendencies that remain statistically reproducible across large numbers of independent dialogue sessions. To translate these modal concepts into empirically tractable measurements, this paper introduces an instantiated observation space consisting of two groups of behavioural dimensions. One group of dimensions is devised for tracking intra-session temporal evolution under the transient mode, while the other group is designed for estimating cross-session statistical baselines under the persistent mode. We emphasise explicitly that the concrete number of dimensions presented here serves only as a working instance for demonstration and experimental deployment. The framework itself does not impose a fixed dimensionality constraint; subsequent researchers are free to add, remove or redefine dimensions according to model type, task domain and research purpose. Our analysis shows that the dual-mode multidimensional architecture can effectively separate instantaneous evolutionary regularities from long-run steady-state regularities of AI behaviour, and provides precise handles for capturing intra-session behavioural drift and cross-session behavioural stability. This framework offers a fresh theoretical paradigm and an actionable empirical pathway for behavioural quantification, mechanism interpretation and alignment governance for artificial-intelligence systems.