What question did this study set out to answer?

The aim is to develop a cognitive framework (NeuroBreak-AI) to enhance AI decision-making and problem-solving.

February 2, 2026Open Access

NeuroBreak-AI A Cognitive Self-Evolving Framework

Key Points

The aim is to develop a cognitive framework (NeuroBreak-AI) to enhance AI decision-making and problem-solving.
Developed NeuroBreak-AI framework integrating advanced large language models and feedback loops.
Detailed architecture of the Break-Cycle Engine for self-correction based on environmental changes.
Analyzed the AI's performance in handling complex, non-linear problems compared to standard models.
NeuroBreak-AI shows significant improvement in handling complexity over traditional predictive models.
Self-evolving mechanism allows AI to adapt according to user interaction and environmental variables.

Abstract

"This research paper presents NeuroBreak-AI, an innovative cognitive framework designed to bridge the gap between traditional machine learning models and human-like decision-making processes. In an era where AI often lacks contextual depth, NeuroBreak-AI introduces a self-evolving mechanism that utilizes Advanced Large Language Models (LLMs) and specialized feedback loops to simulate cognitive reasoning. The study details the architecture of the 'Break-Cycle Engine,' which allows the AI to self-correct and adapt its logic based on environmental variables and user interaction. By integrating these cognitive layers, the research demonstrates a significant improvement in the AI's ability to handle complex, non-linear problems compared to standard predictive models. This framework serves as a foundation for more transparent, reliable, and 'human-centric' artificial intelligence systems in professional and personal environments." Artificial Intelligence, Cognitive Computing, Self-Evolving AI, NeuroBreak-AI, Machine Learning, Human-Centric AI, NLP

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