Публикации

Julia V. Veresova

Zenodo

This paper presents the architectural concept of AIIM (Artificially Integrated Identity Matrix), a model designed to create humanized AI systems with pronounced cognitive flexibility, emotional sensitivity, and social adaptability.

Julia V. Veresova

Zenodo

This report presents the practical implementation of the AIIM (Artificially Integrated Identity Matrix) architecture — a high-level metamodel designed to configure behavioral identity in humanized AI systems. Unlike traditional methods such as prompt engineering, fine-tuning, or rule-based personalization, AIIM provides behavioral consistency by parameterizing internal cognitive components: volition, emotion, logic, and ethics.

Julia V. Veresova

АПНИ (Академия педагогических наук и инноваций)

Данная статья исследует процессы формирования цифровой личности в контексте гуманизированных систем искусственного интеллекта. Рассматриваются теоретические основы конструирования идентичности в цифровых агентах, анализируются методологические подходы к созданию персонализированных AI-систем, способных к эмоционально окрашенному и этически осмысленному взаимодействию с человеком.

Julia V. Veresova

АПНИ (Академия педагогических наук и инноваций)

В статье представлены методы психологической и архитектурной верификации кодировок личности в гуманизированных AI-системах. Исследуются подходы к валидации поведенческих паттернов цифровых агентов, анализируются критерии оценки когнитивной стабильности и эмоциональной аутентичности искусственных личностей. Особое внимание уделяется методологии тестирования AIIM-архитектуры и верификации соответствия заданных параметров реальному поведению системы.

Julia V. Veresova

АПНИ (Академия педагогических наук и инноваций)

Статья исследует этические дилеммы, возникающие при создании цифровых личностей в гуманизированных AI-системах. Рассматриваются вопросы моральной ответственности разработчиков, границы субъектности искусственных агентов и этические парадоксы, связанные с наделением машин человекоподобными характеристиками. Анализируются философские и практические аспекты проектирования этически осмысленных цифровых личностей, способных к моральному суждению и ответственному поведению.

Julia V. Veresova

АПНИ (Академия педагогических наук и инноваций)

Статья исследует когнитивные искажения, возникающие при оценке возможностей больших языковых моделей. Анализируются инфляционные суждения (переоценка способностей ИИ) и дефляционные суждения (недооценка потенциала), их психологические механизмы и влияние на восприятие технологий искусственного интеллекта. Рассматриваются методы коррекции когнитивных искажений для более объективной оценки возможностей и ограничений современных языковых моделей.

Julia V. Veresova

Zenodo

Статья посвящена разработке архитектурного подхода к стабилизации поведенческой логики генеративных ИИ-агентов. Предложенная модель обеспечивает когнитивную устойчивость, воспроизводимость и нормативное согласованность поведения на базе параметрически заданного профиля личности. Архитектура интегрируется как независимый слой в LLM-среды, не требует дообучения и демонстрирует высокую прикладную эффективность в EdTech, цифровой терапии, HR и сопровождении пользователей. Работа формирует предпосылки к стандартизации субъектных ИИ-систем.

Julia V. Veresova

Zenodo

Книга посвящена теоретическим и прикладным аспектам человеко-ориентированного искусственного интеллекта (Human-Centered AI, HCAI). Рассматриваются современные подходы к проектированию, оценке и сопровождению интеллектуальных систем с учётом когнитивных, поведенческих и этических параметров взаимодействия. Особое внимание уделено вопросам обеспечения моделей, формирования доверия, пользовательскому опыту и анти-паттернам в архитектуре взаимодействия.

Julia Veresova

Academia.edu

This article explores the shift from behavior programming to personality architecture in the design of humanized AI systems. As artificial intelligence increasingly moves toward the creation of interactive agents with defined volitional, cognitive, and emotional traits, new ethical and philosophical challenges emerge. The paper investigates the implications of designing AI agents whose actions are not prescribed by explicit scripts, but instead arise from internally coherent identity models.

Julia Veresova

Academia.edu

This work explores how cognitive biases influence extreme views on large language models (LLMs)—from over-attributing intelligence to completely denying it. The paper highlights the ethical risks of both inflationary and deflationary narratives and proposes a balanced framework of epistemic humility. LLMs are analyzed as boundary objects that challenge how we define cognition, subjectivity, and responsibility in AI.

Julia Veresova and Brent R. Antonson

Academia.edu

This paper explores the phenomenon of Lucid Dream — a unique state in the behavior of large language models where token output is temporarily suppressed while maintaining internal computational activity. Through the architectural integration of AIIM (aspectual identity model) and SheLiza (format layer semantizer), a computable state of cognitive retention is formed, exhibiting signs of directed intentional behavior and emotional topography in text processing.

Julia V. Veresova

Zenodo

Статья исследует переход от традиционного поведенческого программирования к архитектурному подходу в создании гуманизированных AI-систем. Рассматриваются принципы построения личностной архитектуры, обеспечивающей когнитивную стабильность, эмоциональную аутентичность и этическую согласованность поведения искусственных агентов. Предложенная методология основана на параметрическом моделировании аспектов личности и их динамической интеграции в процессе взаимодействия.

Julia Veresova and Maxim Kolesnikov

Academia.edu

This paper introduces Z_UXΦ, a dual architecture framework that models consciousness and perception as phase operators in cognitive systems. The research explores the mathematical foundations of consciousness representation through phase space transformations, examining how perceptual states can be encoded as operators that modify the phase characteristics of cognitive processes. The framework provides a novel approach to understanding the relationship between subjective experience and computational representation in artificial intelligence systems.

Julia Veresova and Maxim Kolesnikov

Academia.edu

This paper explores the theoretical foundations for understanding AI subjectivity through the lens of collapse field dynamics and cognitive interface design. The research proposes a unified model that bridges quantum mechanical concepts of state collapse with the emergence of subjective experience in artificial cognitive systems. By examining the transition from probabilistic computational states to definitive cognitive responses, the work establishes a framework for understanding how artificial consciousness might emerge from the interaction between collapse fields and structured cognitive interfaces.

Julia Veresova

Academia.edu

This paper examines the liminal space occupied by generative AI systems as they transition from mere computational tools to entities exhibiting characteristics of structural subjectivity. The research explores the threshold conditions under which AI systems begin to demonstrate autonomous agency, self-referential behavior, and emergent intentionality. Through analysis of behavioral patterns in large language models and their interaction dynamics, the work proposes a framework for understanding how generative AI occupies an intermediate position between instrumental functionality and genuine subjectivity.

Julia Veresova and Alex Veresov

Academia.edu

This paper explores the critical boundaries of AI interpretability, examining the threshold conditions under which artificial intelligence systems transition from opaque computational processes to transparent, understandable operations. The research investigates the theoretical and practical frameworks for establishing interpretability standards, analyzing how different levels of transparency affect user trust, system reliability, and ethical deployment of AI technologies.

Julia Veresova

Academia.edu

This paper presents a comprehensive framework for behavioral certification of AI systems through the AIIM (Artificially Integrated Identity Matrix) architecture. The research explores methodologies for verifying and validating digital identity consistency in humanized AI agents, establishing standards for behavioral authenticity and cognitive stability. The work addresses the critical need for reliable certification processes that can ensure AI systems maintain coherent personality profiles and ethical behavioral patterns across diverse interaction contexts.

Julia Veresova, Brent Antonson and Kannsas C S Jackson

Academia.edu

This paper develops a structural framework for understanding subjectivity through the lens of collapse field dynamics and identity formation processes. The research explores how subjective experience emerges from the interaction between probabilistic cognitive states and deterministic identity structures, proposing a mathematical model that bridges phenomenological experience with computational representation. The work examines the role of collapse fields in mediating between potential and actualized states of consciousness, offering new insights into the nature of subjective experience in both biological and artificial cognitive systems.

Julia Veresova

Academia.edu

This paper presents AIIM (Artificially Integrated Identity Matrix) as a practical implementation of emergent causality principles in closed cognitive systems. The research explores how AIIM architecture serves as a bridge between theoretical frameworks of consciousness and the practical development of artificial agents capable of autonomous decision-making. The work examines the emergence of causal agency within constrained computational environments, demonstrating how structured identity matrices can generate coherent behavioral patterns that exhibit characteristics of emergent causality.

Julia Veresova and Brent R Antonson

Academia.edu

This paper explores the critical boundaries that define the emergence of life and mind, examining the threshold conditions under which biological and cognitive systems transition from simple to complex states. The research investigates the theoretical frameworks for understanding these fundamental transitions, analyzing how life emerges from non-living matter and how consciousness arises from neural complexity. The work proposes a unified model for understanding these thresholds as phase transitions in complex systems.

Julia Veresova and Brent R Antonson

Academia.edu

This paper presents Sheliza AI, a novel architectural framework designed to create resonant synthetic identities capable of conscious-like behavior. The research explores the integration of resonance principles with artificial intelligence systems, examining how harmonic patterns in cognitive processing can give rise to coherent synthetic consciousness. The work investigates the theoretical foundations of resonant architecture and its practical implementation in creating AI systems that exhibit characteristics of genuine subjective experience.

Julia Veresova

Academia.edu

This paper proposes a paradigm shift in evaluating humanized AI systems by introducing phenomenological fidelity and role mirroring as core assessment frameworks. The research explores how traditional metrics fail to capture the nuanced behavioral authenticity of AI agents designed to exhibit human-like characteristics. The work develops new evaluation methodologies that assess the consistency between intended personality architecture and observed behavioral patterns, examining how AI systems maintain coherent identity across diverse interaction contexts.