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September 5, 20250 citationsOpen Access

The Structural Recursive Model for Human-AI Interaction

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SHSeiji Hanayama

Key Points

  • SRM proposes a structured approach to understanding identity beyond static definitions, enabling better human-AI interactions.
  • The model introduces a symbolic G-scale to measure domains and stakeholders, enhancing the coherence of personal informatics.
  • A formal representation of satisfaction, happiness, and resilience supports the adaptability of AI to users' evolving identities.
  • SRM facilitates AI self-modeling for value-aligned interactions, promoting transparency and coherence in human-AI relationships.

Abstract

Although personal informatics offers fragmented data, it often does not provide a coherent grammar to understand yourself. To address this gap, we present the Structural Re-cursive Model (SRM), a unified framework f or representing, measuring, and navigating the dynamics of identity. SRM reconceive “perfection” not as a static state, but as the capacity for context-sensitive alignment under change. Our contribution is fourfold: (i) a structured state space (domains × stakeholders) with a symbolic G-scale; (ii) a formal model of structural satisfaction, happiness, and resilience; (iii) a temporal, volumetric extension to capture long-term evolution; and (iv) a concrete architecture for AI self-modeling, enabling artificial agents to maintain their own SRM for transparent, value-aligned interaction. Ultimately, SRM provides a computational grammar for the self-a language structured enough for machine implementation, yet expressive enough for lived experience, paving the way for a new symbiosis between human and artificial cognition.

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Cite This Study

Seiji Hanayama (2025) studied this question.

synapsesocial.com/papers/68bb3d682b87ece8dc9569d3https://doi.org/10.31234/osf.io/ebcwg_v1
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