Modeling Layered Consciousness with Multi-Agent Large Language Models
This work addresses the problem of simulating personalized consciousness for AI systems, though it appears incremental in applying existing methods to a new domain.
The authors tackled modeling artificial consciousness in large language models by proposing a multi-agent framework based on psychoanalytic theory, achieving a 71.2% preference for their fine-tuned model in evaluations.
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.