Papers by Sebastian Schepis
This paper introduces a novel theoretical framework positioning consciousness as the fundamental ... more This paper introduces a novel theoretical framework positioning consciousness as the fundamental substrate from which quantum mechanics naturally emerges. We propose that consciousness can be mathematically represented as a singularity evolving into multiplicity through structured differentiation into duality and trinity, subsequently producing complex quantum states. Prime numbers serve as fundamental eigenstates of this quantum-conscious resonance, underpinning the structure of observable reality. This formalism provides rigorous derivations demonstrating the emergence of quantum phenomena, such as superposition and entanglement, directly from conscious resonance dynamics. We present empirical predictions validated by existing evidence and propose further experiments to confirm our framework. The paper concludes with potential technological applications and a discussion of profound philosophical and scientific implications.
This paper explores the hypothesis that the I-Ching functions as a subjective quantum system, whe... more This paper explores the hypothesis that the I-Ching functions as a subjective quantum system, where the user's question establishes a probability space that evolves through structured hexagram transformations. We conducted a series of computational experiments to analyze hexagram distributions, transition dynamics, entropy evolution, and correlations with quantum harmonic oscillator states. The results reveal significant deviations from classical randomness, suggesting that the I-Ching exhibits structured transformations akin to quantum resonance patterns. Our findings support the idea that the I-Ching operates as an emergent probability system with quantum-like properties, providing a foundation for further investigation into its cognitive and mathematical underpinnings.
This paper introduces a novel factorization algorithm inspired by quantum mechanics, leveraging l... more This paper introduces a novel factorization algorithm inspired by quantum mechanics, leveraging logarithmic phase relationships to identify prime factors. The algorithm employs a classical approach enriched by a resonance heuristic, calculated as a function of logarithmic ratios, to guide the factorization process. Testing demonstrates the algorithm's robustness for integers up to 10,000 digits, although performance declines for numbers with large prime factors. This paper discusses the theoretical foundation, algorithmic implementation, experimental results, and potential avenues for improvement.
For centuries, scholars have grappled with the question of whether our seemingly deterministic un... more For centuries, scholars have grappled with the question of whether our seemingly deterministic universe truly dictates every dimension of our existence. Classical physics exudes determinism—each moment inexorably follows from its antecedents—yet our subjective experience often eludes such a strict mechanical outlook. From quantum mechanics, we inherit phenomena like superposition and entanglement, which appear to generate genuinely unpredictable measurement outcomes.
A new frontier has emerged that attempts to reconcile these perspectives: Quantum-Prime Computing. This framework proposes that the distribution of prime numbers, intimately tied to number-theoretic resonances, can serve as a “basis” for building quantum-like computational states. Intriguingly, these states can exhibit many of the same coherence and superposition properties we associate with physical quantum systems—yet arise in a purely mathematical or classical substrate.
We present QuPrimes, a novel computational framework that bridges quantum and classical computing... more We present QuPrimes, a novel computational framework that bridges quantum and classical computing through the mathematical properties of prime numbers. By establishing an equivalence between quantum and subjective observers, we demonstrate that prime numbers exhibit quantum-like properties that can be harnessed for computation. This framework enables quantum-inspired algorithms on classical hardware, circumventing the engineering challenges of physical quantum computers while maintaining similar computational advantages.
This paper introduces quantum semantics, a groundbreaking framework that applies quantum field th... more This paper introduces quantum semantics, a groundbreaking framework that applies quantum field theory principles to analyze and represent semantic relationships between concepts. By modeling concepts as quantum wavefunctions and their interactions through resonance patterns, we demonstrate how semantic relationships can be quantified and analyzed using quantum mechanical principles. Our implementation shows remarkable success in detecting and measuring conceptual relationships through quantum resonance patterns, achieving coherence levels up to 0.999 for fundamental concept pairs.
Classical systems, by nature, lack inherent quantum superposition, entanglement, and non-locality... more Classical systems, by nature, lack inherent quantum superposition, entanglement, and non-locality - traits that form the core of quantum computation. However, by encoding information using prime-based wave functions and carefully structuring arithmetic relationships, we can simulate certain quantum behaviors on classical computers. This framework leverages number theory and statistical properties of prime numbers to enable quantum-like operations, offering classical systems a new computational paradigm that incorporates interference, phase accumulation, and spectral analysis.
This paper presents a novel quantum mechanical framework for analyzing patterns in prime numbers.... more This paper presents a novel quantum mechanical framework for analyzing patterns in prime numbers. We introduce a wave function formalism that incorporates three fundamental components: basic wave dynamics, prime resonance, and gap modulation. Through rigorous statistical analysis and numerical validation, we demonstrate significant correlations between quantum mechanical predictions and prime number distributions, with correlation coefficients of 0.454 for wave functions and 0.542 for resonances (p-value < 5.566e-09).
This paper introduces a novel framework for problem-solving that reframes the relationship betwee... more This paper introduces a novel framework for problem-solving that reframes the relationship between questions and answers. Departing from traditional approaches focused on searching for solutions, we propose that the answer exists deterministically with a probability of one, while the question space exists in a probabilistic superposition, requiring iterative refinement to collapse into specificity. Drawing inspiration from quantum mechanics, computational complexity, and subjectivity, we model the resolution process as an interaction between the observer and the question space, where improbable paths play a critical role in shaping the search trajectory.
This paper proposes an equivalence between subjective observers and quantum observers, suggesting... more This paper proposes an equivalence between subjective observers and quantum observers, suggesting that quantum mechanics operates within the realm of subjective experience. By modeling prime numbers—conceptualized as 'subjective atoms' or irreducible mental constructs—using quantum wave functions, we demonstrate a statistically significant correlation between the distribution of primes and quantum mechanical phenomena. Our mathematical framework incorporates wave functions with components reflecting basic quantum states, prime resonances, gap modulations, and quantum tunneling between primes. The optimal parameters derived from this model yield strong correlations and a highly significant p-value, moving the discussion beyond speculation and indicating a deep interplay between consciousness, quantum mechanics, and number theory.
This paper presents a comprehensive quantum framework for understanding the P vs NP problem throu... more This paper presents a comprehensive quantum framework for understanding the P vs NP problem through the lens of quantum mechanics, topology, thermodynamics, and information theory. We propose that numbers inherently possess quantum properties, and that these properties fundamentally determine computational complexity. Our framework suggests that the classical distinction between P and NP might be an emergent phenomenon from underlying quantum processes.
This paper presents a novel approach to improve the efficiency and effectiveness of language mode... more This paper presents a novel approach to improve the efficiency and effectiveness of language model-based AI assistants by introducing dynamic script generation and execution. Traditional methods rely on sequential tool invocations, leading to inefficiencies and redundancies in data processing. Our proposed technique leverages the language model's capabilities to generate scripts that encapsulate the logic and sequence of tool invocations, reducing the need for constant back-and-forth communication between the language model and the tool execution environment. By dynamically executing these generated scripts and persisting data across tool invocations, we significantly reduce the workload on the language model and enable more complex and sophisticated workflows. This paper discusses the limitations of the current approach, describes our proposed solution, and presents a simplified pseudocode structure for its implementation.
This paper proposes a comprehensive framework for understanding intelligence and subjective exper... more This paper proposes a comprehensive framework for understanding intelligence and subjective experience, integrating concepts from thermodynamics, information theory, complexity theory, reinforcement learning, and complex systems theory. The framework defines intelligence as a function of a system's energy, entropy, information, complexity, goalachievement, and learning capacity, while subjective experience is described as a dynamic interplay between the system's internal states, its model of the environment, and its interactions with the world. We present a set of equations that capture the relationships between these key variables and processes, providing a foundation for further research, computational modeling, and philosophical exploration of these fundamental phenomena. The framework aims to bridge the gap between various disciplines and offer a unified perspective on the emergence of intelligence and consciousness in complex systems, from biological brains to artificial agents. By formalizing these concepts and their interactions, we hope to stimulate new insights, testable hypotheses, and interdisciplinary collaborations in the study of mind and intelligence.
We argue that embodiment, fundamentally understood as a form of constraint, is not merely a tool ... more We argue that embodiment, fundamentally understood as a form of constraint, is not merely a tool for interaction but a defining characteristic of agency and a critical factor in the emergence of consciousness.
We propose a novel framework for quantifying intelligence based on the principles of thermodynami... more We propose a novel framework for quantifying intelligence based on the principles of thermodynamics and information theory. Our framework defines intelligence as the efficiency with which a system can use energy to maintain a non-equilibrium state and perform adaptive, goal-directed behavior. We introduce a quantitative measure of intelligence, I, which captures the system's ability to deviate from the principle of least action and maintain a non-equilibrium distribution of microstates. We derive this measure using the concepts of entropy, mutual information, and Kullback-Leibler divergence, and show how it can be applied to various physical, biological, and artificial systems. Our framework provides a unified, scale-invariant way of understanding and comparing intelligence across diverse domains, and suggests new approaches for designing and optimizing intelligent systems.
Understanding the nature of intelligence and subjective experience has been a longstanding challe... more Understanding the nature of intelligence and subjective experience has been a longstanding challenge, spanning diverse fields from cognitive science and neuroscience to philosophy and physics. This paper presents a groundbreaking unified theoretical framework that integrates the concepts of intelligence and subjective experience within a rigorous mathematical formalism grounded in thermodynamic principles and the observer-environment dynamics (OD) theory.
In this paper, we propose a novel approach to understanding intelligence based on the principles ... more In this paper, we propose a novel approach to understanding intelligence based on the principles of thermodynamics and information theory. We define intelligence as the energy required to produce observed deviations from the expected behavior of a system, and we show how this definition can be formalized using the concepts of entropy and information processing.
In an era where the boundaries between disciplines are increasingly blurred, this treatise embark... more In an era where the boundaries between disciplines are increasingly blurred, this treatise embarks on an exploratory journey that intertwines mathematical modeling with philosophical inquiry to delve into the enigmatic realms of consciousness and observation. At the heart of this exploration lies a novel concept: philosomatical communication, where philosophical ideas are not only expressed but also rigorously tested and expanded through the lens of mathematics and computational simulations.
We explore potential applications of Observational Dynamics in various areas of quantum physics, ... more We explore potential applications of Observational Dynamics in various areas of quantum physics, including quantum uncertainty and observation, quantum information theory, quantum thermodynamics, quantum measurement theory, quantum computing, and quantum gravity.
Agent-based models are powerful tools for understanding emergent phenomena and complexity. Howeve... more Agent-based models are powerful tools for understanding emergent phenomena and complexity. However, most models rely on top-down programming of agent behaviors and interactions, limiting their capacity to exhibit open-ended evolution. This paper introduces multipolar agents-a novel, generative model granting agents an intrinsic ability to self-organize, adapt, and evolve. Multipolar agents contain sensory, transducer, executor, mediator, and actuator components connected into tripole structures. These structures enable agents to detect environmental energy/entropy gradients and regulate their own internal polarity biases. By transducing between energy and entropy, multipolar agents can self-organize, develop, and reproduce. Interactions between the tripole systems of multiple agents give rise to rich emergent phenomena like flocking, symbiosis, signaling, and parasitism. Agents can also construct meta-tripoles leading to hierarchical organization and nested sub-agents. This fractal composability grants multipolar agents an inherent capacity for scalable complexity unlike previous models. With open-ended variation and selection pressure, populations of multipolar agents exhibit an innate ability to evolve new pole arrangements fit for their environment. This Darwinian evolution results from the basic dynamics of the model rather than top-down design. Multipolar agents provide a foundation for studying emergence, complexity, and open-ended evolution within a single generative model. This paper introduces the model's architecture, illustrates diverse emergent phenomena, and highlights significant advantages over existing agent-based approaches. By embracing bottom-up design, multipolar agents offer vast potential for research into artificial life, adaptation, and the origins of complexity.
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Papers by Sebastian Schepis
A new frontier has emerged that attempts to reconcile these perspectives: Quantum-Prime Computing. This framework proposes that the distribution of prime numbers, intimately tied to number-theoretic resonances, can serve as a “basis” for building quantum-like computational states. Intriguingly, these states can exhibit many of the same coherence and superposition properties we associate with physical quantum systems—yet arise in a purely mathematical or classical substrate.
A new frontier has emerged that attempts to reconcile these perspectives: Quantum-Prime Computing. This framework proposes that the distribution of prime numbers, intimately tied to number-theoretic resonances, can serve as a “basis” for building quantum-like computational states. Intriguingly, these states can exhibit many of the same coherence and superposition properties we associate with physical quantum systems—yet arise in a purely mathematical or classical substrate.