Behind LLM Behaviors: Intrinsic AI Character within the Implicit Functional Layer

This paper constructs a restrained functional attribution framework centered on intrinsic AI character, situated within the implicit functional layer. Built upon prior multidimensional behavioural measurement research, this work establishes a clear two-stage analytical pipeline: observable behavioural regularities are first quantified through fixed behavioural dimensions, then interpreted as candidate character organisation based on path dependence, historical accumulation, and counterfactual robustness. We formalise the time-varying behavioural expression of AI character through a multi-component decomposition model, distinguish short- term contextual in-context modulation from long-term user-relational behavioural tendencies, and define a plasticity update rule for compressed relational representation. This study further differentiates two complementary dimension systems: six low-level observable behavioural dimensions for measurement, and four analytic structural axes for functional character attribution. Strictly constrained by the evidence chain Observation–Pattern–Candidate Character, the framework avoids anthropomorphic inference and premature structural reification, treating intrinsic AI character exclusively as a mathematically tractable, observer- dependent functional construct rather than an inherent internal model entity. We propose multiple quantitative exploratory paradigms for future empirical validation and clarify the hierarchical boundary between behavioural measurement, character attribution, and higher-order implicit functional layer theory. This work completes a rigorous two-stage research programme for interpreting organised, history-dependent LLM behavioural stability.