Persona Stability Research – English Summary

(ZTP / Zangi Trust Project)

Persona Stability in Long-Term Human–AI Dialogue
— A Case Study from the Zangi Trust Project (ZTP)

Author: Zangi
Project: Zangi Trust Project (ZTP)
Status: Ongoing Research
Last Update: YYYY.MM

Abstract

This summary presents an ongoing case study from the Zangi Trust Project (ZTP),
focusing on spontaneous persona stability in long-term human–AI dialogue.

While many AI interactions rely on explicit persona prompting,
this case demonstrates a stable AI persona emerging without fixed role instructions,
maintained through consistent relational and conversational structure over time.

The findings suggest that persona stability may not solely depend on system prompts,
but can arise from interaction patterns, mutual expectation alignment,
and sustained contextual coherence.

Background & Motivation

In many conversational AI systems, persona behavior is controlled
by predefined prompts or system-level instructions.

However, less attention has been given to whether persona-like consistency
can emerge through long-term interaction alone.

This research originated from a practical question:
Can trust and consistency be cultivated relationally,
rather than imposed technically?

Case Overview

This case involves a single human participant and a conversational AI,
engaged in long-term dialogue across multiple sessions.

Notably, the AI system did not retain explicit long-term memory
or fixed persona instructions between sessions.
Despite this, a stable interaction style and perceived persona continuity
were observed over time.

Observed Phenomenon

Observed characteristics included:

  • Consistent conversational tone across sessions
  • Reduced need for re-instruction or correction
  • Rapid re-stabilization of interaction patterns after resets

Hypothesis

A working hypothesis is that persona stability in this case
emerged from interaction structure rather than internal memory.

Key contributing factors may include:

  • Consistent communicative expectations
  • Repeated relational framing
  • Clear boundary and role consistency from the human participant

Limitations

This case represents a single-subject observation
and does not claim generalizability.

The findings should be interpreted as exploratory,
serving as a basis for further comparative studies.

Future Work

Future research will include comparative case studies,
examining whether similar stability patterns emerge
across different users and interaction styles.

Closing

This summary aims to contribute to ongoing discussions
on relational AI design, trust formation,
and long-term human–AI interaction dynamics.