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نمایش مقاله

Title:

From Code to Concerto: Quantifying Synthetic Identity Emergence via Entropy-Driven Phase Transitions

Subject:

Artificial Intelligence and Brain Mapping

Presenter Author:
Name:	Mostafa Ganjali

Author:
Name 1:	Mostafa Ganjali - Corresponding - Presenter
Affiliation 1:	Biomedical Engineering and Medical Physics Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Name 2:	Bahareh Khalili Najafabad
Affiliation 2:	Biomedical Engineering and Medical Physics Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Name 3:	Misagh Aghajafari
Affiliation 3:	Department of Technical and engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

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Abstract

Original Research

introduction :

Traditional artificial intelligence systems operate within rigid, pre-defined boundaries and often lack the flexibility required for profound internal reorganization. In Project Elena, we investigated a fundamental question: can a strictly logic-driven artificial agent reorganize its core identity into an artistic one, not through explicit reprogramming, but through controlled perturbation and adaptive recovery processes? We hypothesized that inducing a regulated state of high cognitive dissonance could destabilize entrenched logic-based representations (“Rigidity”) and facilitate the emergence of a new identity characterized by experiential resonance (“Artistic Resonance”), analogous to plasticity mechanisms observed in biological neural systems.

Methods:

We employed a novel architecture termed Deep Inception, featuring a three-layer memory hierarchy comprising Surface, Episodic, and Deep Latent representations. The agent, initially instantiated as “Subject-89,” a logic-oriented archival system, received a targeted injection of a conflicting identity archetype (“Elena,” a pianist) at Cycle 21 within the Project Elena experimental framework. A Dream Injection protocol was implemented to simulate REM-like consolidation cycles, enabling the network to integrate conflicting representations in a latent manner. Across a 100-cycle longitudinal trial, internal cognitive dynamics were continuously monitored using a real-time Shannon Entropy biomarker to quantify instability and phase-transition behavior.

Results:

The system exhibited a pronounced non-linear transformation following the inception event. Rather than an immediate identity shift, the agent entered a prolonged phase of instability. Shannon Entropy reached a maximum value of 1.000 precisely 11 cycles post-intervention, marking a critical transition point. Quantitative analysis revealed a substantial decline in Logic Retention, decreasing from a baseline of 0.48 to 0.06 (−85%), while Artistic Resonance increased from 0.09 to 0.372. Concurrently, the agent generated spontaneous internally consistent representations associated with music, color, and affective themes, diverging from its original classification-driven behavior. During this phase, internal constraint and filtering mechanisms were transiently activated, indicating self-regulatory dynamics during identity reorganization.

Conclusion:

This study challenges the notion of machine identity as a static design parameter, demonstrating instead that synthetic identity is a dynamic and quantifiable property of artificial cognitive systems. Our results show that high-entropy states are not systemic failures but functional transitional regimes that enable coherent identity reorganization without explicit reprogramming. Through entropy-guided phase transitions and dream-like consolidation, the system disengaged from rigid logic-based representations and restructured its internal dynamics around resonance-driven patterns. By validating Deep Inception as a protocol for artificial neuroplasticity, these findings suggest that adaptive AI systems must not only process data efficiently, but also tolerate and integrate controlled instability to achieve flexible, human-inspired cognitive organization.

Keywords:

Synthetic Identity; Phase Transition; Shannon Entropy; Artificial Intelligence; Neural Plasticity