mlsanityData to 7 Oct 2026

Humanoid Artificial Consciousness Designed with Large Language Model Based on Psychoanalysis and Personality Theory

arXiv 2510.09043 ↗First posted 10 Oct 2025cs.AIcited by 1

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Abstract (arXiv)

Human consciousness is still a concept hard to define with current scientific understanding. Although Large Language Models (LLMs) have recently demonstrated significant advancements across various domains including translation and summarization, human consciousness is not something to imitate with current upfront technology owing to so-called hallucination. This study, therefore, proposes a novel approach to address these challenges by integrating psychoanalysis and the Myers-Briggs Type Indicator (MBTI) into constructing consciousness and personality modules. We developed three artificial consciousnesses (self-awareness, unconsciousness, and preconsciousness) based on the principles of psychoanalysis. Additionally, we designed 16 characters with different personalities representing the sixteen MBTI types, with several attributes such as needs, status, and memories. To determine if our model's artificial consciousness exhibits human-like cognition, we created ten distinct situations considering seven attributes such as emotional understanding and logical thinking. The decision-making process of artificial consciousness and the final action were evaluated in three ways: survey evaluation, three-tier classification via ChatGPT, and qualitative review. Both quantitative and qualitative analyses indicated a high likelihood of well-simulated consciousness, although the difference in response between different characters and consciousnesses was not very significant. This implies that the developed models incorporating elements of psychoanalysis and personality theory can lead to building a more intuitive and adaptable AI system with humanoid consciousness. Therefore, this study contributes to opening up new avenues for improving AI interactions in complex cognitive contexts.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 10 Oct 2025.

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13 of 52 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2023The Falcon Series of Open Language Modelscited by 526
  6. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 478
  7. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
  8. 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 122
  9. 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 99
  10. 2023Could a Large Language Model be Conscious?cited by 70
  11. 2023Can ChatGPT Assess Human Personalities? A General Evaluation Frameworkcited by 69
  12. 2024Prompt Design and Engineering: Introduction and Advanced Methodscited by 45
1 more
  1. 2024The Drama Machine: Simulating Character Development with LLM Agentscited by 5

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