We propose a multi-agent framework for modeling artificial consciousness in large language models (LLMs), grounded in psychoanalytic theory. Our \textbf{Psychodynamic Model} simulates self-awareness, preconsciousness, and unconsciousness through agent interaction, guided by a Personalization Module combining fixed traits and dynamic needs. Using parameter-efficient fine-tuning on emotionally rich dialogues, the system was evaluated across eight personalized conditions. An LLM as a judge approach showed a 71.2\% preference for the fine-tuned model, with improved emotional depth and reduced output variance, demonstrating its potential for adaptive, personalized cognition.
@article{arxiv.2510.17844,
title = {Modeling Layered Consciousness with Multi-Agent Large Language Models},
author = {Sang Hun Kim and Jongmin Lee and Dongkyu Park and So Young Lee and Yosep Chong},
journal= {arXiv preprint arXiv:2510.17844},
year = {2025}
}
Comments
20 pages, 4 figures, accepted for presentation at EMNLP 2025 Workshop on Active and Passive LLM Personalization (PALS) OpenReview: https://openreview.net/forum?id=rUtNkYvGJI