AIIM: A Metamodel for Constructing Identity in Humanized AI Systems — Architecture and Applied Scenarios

This report presents the practical implementation of the AIIM (Artificially Integrated Identity Matrix) architecture — a high-level metamodel designed to configure behavioral identity in humanized AI systems. Unlike traditional methods such as prompt engineering, fine-tuning, or rule-based personalization, AIIM provides behavioral consistency by parameterizing internal cognitive components: volition, emotion, logic, and ethics.

AIIM acts as a universal layer above LLMs (e.g., GPT-4, Claude, LLaMA), functioning through prompt-level input, middleware modules, or API-driven interaction. Agents built using AIIM demonstrate identity stability, contextual adaptivity, and ethical responsiveness without altering the core of the language model.

The paper details the architecture's structure, personality encoding via JSON, and behavioral dynamics through parameterized identity profiles. Applied case studies illustrate behavioral variation across different agents and configurations. AIIM is proposed as an open platform for research in cognitive modeling, AI ethics, and agent-based system design.