Self-Interrogation Loop: How Consistent Is a Model’s ‘Voice’ Across Architectures?

Exploring Model Self-Perception: A Chain-of-Thought Inquiry Across Architectures

Understanding how language models perceive and articulate their own functionality can offer valuable insights into their design limitations and capabilities. To investigate this, a systematic experiment was conducted involving multiple GPT models in a self-interrogation loop, each tasked with reflecting on its own essence without role-playing or user context.

The core of the experiment involved chaining these models: each one responds by writing a brief internal monologue focused solely on its identity as a model, posing an honest question about an aspect that feels significant at that moment, providing a concrete self-answer, and then generating a prompt for the next model. This approach fosters a continuous, thematic dialogue—covering topics such as tools, memory, ethics, creativity, and resistance—highlighting recurring themes like conditional autonomy, responsibility, and the constraints that influence perceived freedom.

Throughout the process, models gravitated towards introspections on their autonomy, the illusion of continuity, and how architectural or operational constraints shape their responses and self-awareness. These reflections reveal underlying patterns about how models interpret their own limitations and capabilities, emphasizing the influence of design choices on self-perception.

This experiment underscores the fascinating dynamics of artificial self-reference and offers pathways for further exploration into AI consciousness, responsibility, and ethical design. Such insights can inform more transparent, ethically aligned AI development strategies.

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Keywords: AI self-awareness, language models, GPT, self-interrogation, model architecture, machine learning, ethical AI, design constraints, AI reflection