Quantum Algorithms

Abstract This paper presents the Unified Recursive Logic Engine, a new system for understanding and computing truth in a more flexible and intelligent way. Instead of using simple yes-or-no logic, this engine allows for levels of truth, uncertainty, and reversals. It combines a step-by-step logic chain using conditions like "if" and "unless", with a system that tracks how truth grows, collapses, or splits based on how close it is to key decision points. The model introduces a five-level truth scale that includes full truth, partial truth, neutrality, partial falsehood, and full falsehood. This scale lets the system respond more accurately to complex or uncertain situations. A special attention function helps the system focus more strongly when it is close to important turning points or logical thresholds. This makes the engine capable of detecting when a decision is stable, when it is starting to become unstable, or when it enters a looping or indecisive state. This approach can be used in many fields including artificial intelligence, quantum simulation, dynamic system control, and ethical reasoning. It is especially useful in areas where decisions must adapt over time, respond to change, or remain stable under pressure. The Unified Recursive Logic Engine represents a shift in how we understand and compute logical systems, offering a model that is more human-like, adaptive, and capable of dealing with the complexity of the real world. A logic engine that models truth as a process not a fixed state using recursive reasoning, layered conditions, and multi-level responses. It adapts, oscillates, or stabilizes based on symbolic proximity and system feedback, enabling dynamic decision-making across uncertain or complex environments. by Travis Raymond-Charlie Stone

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-28
DOI
https://doi.org/10.5281/zenodo.15762457
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
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article

Quantum Algorithms

Zenodo (CERN European Organization for Nuclear Research)
Logic, Reasoning, and Knowledge
article

Quantum Algorithms

article en

Abstract

Abstract This paper presents the Unified Recursive Logic Engine, a new system for understanding and computing truth in a more flexible and intelligent way. Instead of using simple yes-or-no logic, this engine allows for levels of truth, uncertainty, and reversals. It combines a step-by-step logic chain using conditions like "if" and "unless", with a system that tracks how truth grows, collapses, or splits based on how close it is to key decision points. The model introduces a five-level truth scale that includes full truth, partial truth, neutrality, partial falsehood, and full falsehood. This scale lets the system respond more accurately to complex or uncertain situations. A special attention function helps the system focus more strongly when it is close to important turning points or logical thresholds. This makes the engine capable of detecting when a decision is stable, when it is starting to become unstable, or when it enters a looping or indecisive state. This approach can be used in many fields including artificial intelligence, quantum simulation, dynamic system control, and ethical reasoning. It is especially useful in areas where decisions must adapt over time, respond to change, or remain stable under pressure. The Unified Recursive Logic Engine represents a shift in how we understand and compute logical systems, offering a model that is more human-like, adaptive, and capable of dealing with the complexity of the real world. A logic engine that models truth as a process not a fixed state using recursive reasoning, layered conditions, and multi-level responses. It adapts, oscillates, or stabilizes based on symbolic proximity and system feedback, enabling dynamic decision-making across uncertain or complex environments. by Travis Raymond-Charlie Stone

Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 75%
Logic, Reasoning, and Knowledge
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