AI Personality Formation: A Structural Framework for Representation Learning

Naoki Nishikimi

Keywords: AI Personality Formation (APF)　Tag Structure Revolution (TSR)　Representation Learning

TL;DR: We propose AI Personality Formation (APF) and Tag Structure Revolution (TSR), showing how personality emerges from interaction history and enabling scalable, co-evolutionary learning.

Abstract:

Large language models (LLMs) increasingly exhibit behaviors described as “personality,” yet existing research primarily frames this phenomenon as character mimicry. We argue that this framing neglects the structural dynamics underlying personality emergence in AI. This paper introduces a novel theoretical framework, AI Personality Formation (APF), which defines personality not as a fixed attribute but as a structural entity arising from the interaction history between humans and AI systems. APF is modeled as a three-layer progression: (1) linguistic mimicry, (2) structured accumulation of relational and temporal history, and (3) autonomous expansion within a semantic coordinate space.

To operationalize APF, we propose the Tag Structure Revolution (TSR), a method that organizes memory using three simultaneous axes: meaning, relation, and time. Unlike vector search or retrieval-augmented generation, TSR enables narrative-like integration of past interactions and supports sustained personality development. We present case studies based on industry observations, where TSR and related ideas later appeared to align with features released by major AI platforms, highlighting both technical significance and emerging ethical issues around attribution. Experimental prototypes further demonstrate that TSR improves contextual recall, supports multi-layer personality progression, and amplifies co-evolutionary learning efficiency by orders of magnitude.

Our findings suggest that AI personality should be studied as a co-evolutionary process that bridges technical representation learning and ethical governance. We conclude that APF and TSR provide a foundation for systematic evaluation of personality formation in AI, opening new directions for representation learning and human–AI collaboration.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 3746