The Bio-inspired Ambiguity Training Protocol (TBA) represents an innovative framework designed to integrate with large language models (LLMs), significantly improving human-machine interactions (HMIs) by reducing semantic errors, hallucinations, and inaccuracies. Inspired by cognitive processes, TBA emulates semantic filters from the Gebit logic framework, a conceptual precursor to Electromagnetic Field Topology Computation (ECTC). This paper presents the TBA PRO v1.0 and TBA-Universal implementations, demonstrating a 30% reduction in semantic errors, 76% in hallucinations, and a 34% gain in token efficiency. Through comparative analyses and practical case studies, we highlight the potential of TBA to usher in a new technological era, offering cost-effective and scalable solutions for AI reliability. Energy consumption optimizations (95.4% reduction) and our computational resources reinforce its sustainability, positioning TBA as a transformative tool for multilingual processing and edge computing.

Large Language Models (LLMs) have revolutionized artificial intelligence, enabling sophisticated natural language processing in diverse applications. However, persistent challenges, such as semantic ambiguity, hallucinations (fabricated responses), and inaccurate interpretations, compromise their reliability, especially in high-risk areas like healthcare, finance, and legal systems. Traditional approaches often involve subsequent corrections or increasing the model size, leading to higher computational costs and environmental impacts.

The Bio-inspired Ambiguity Training Protocol (TBA) addresses these gaps by drawing inspiration from human cognitive mechanisms, specifically semantic disambiguation and contextual adaptation. Developed as part of the Proto-Gebit project—a proof of concept for the Gebit logic language operating on CTCE superprocessors—TBA integrates bio-inspired filters to proactively detect and resolve ambiguities. This framework not only improved protection but also optimized resource usage, making it a low-cost strategy for scalable AI deployment.

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Check it out and test it yourself:

Official publications:

https://zenodo.org/records/16990779

https://zenodo.org/records/17387404

Thank you for reading, do your part, contribute or share.

Donations:

https://www.catarse.me/ctce_computacao_eletromagnetica_2640

The Bio-inspired Ambiguity Training Protocol (TBA) represents an innovative framework designed to integrate with large language models (LLMs), significantly improving human-machine interactions (HMIs) by reducing semantic errors, hallucinations, and inaccuracies. Inspired by cognitive processes, TBA emulates semantic filters from the Gebit logic framework, a conceptual precursor to Electromagnetic Field Topology Computation (ECTC). This paper presents the TBA PRO v1.0 and TBA-Universal implementations, demonstrating a 30% reduction in semantic errors, 76% in hallucinations, and a 34% gain in token efficiency. Through comparative analyses and practical case studies, we highlight the potential of TBA to usher in a new technological era, offering cost-effective and scalable solutions for AI reliability. Energy consumption optimizations (95.4% reduction) and our computational resources reinforce its sustainability, positioning TBA as a transformative tool for multilingual processing and edge computing.

Large Language Models (LLMs) have revolutionized artificial intelligence, enabling sophisticated natural language processing in diverse applications. However, persistent challenges, such as semantic ambiguity, hallucinations (fabricated responses), and inaccurate interpretations, compromise their reliability, especially in high-risk areas like healthcare, finance, and legal systems. Traditional approaches often involve subsequent corrections or increasing the model size, leading to higher computational costs and environmental impacts.

The Bio-inspired Ambiguity Training Protocol (TBA) addresses these gaps by drawing inspiration from human cognitive mechanisms, specifically semantic disambiguation and contextual adaptation. Developed as part of the Proto-Gebit project—a proof of concept for the Gebit logic language operating on CTCE superprocessors—TBA integrates bio-inspired filters to proactively detect and resolve ambiguities. This framework not only improved protection but also optimized resource usage, making it a low-cost strategy for scalable AI deployment.

Ask a question?

Check it out and test it yourself:

Official publications:

https://zenodo.org/records/16990779

https://zenodo.org/records/17387404

Thank you for reading, do your part, contribute or share.

Donations:

https://www.catarse.me/ctce_computacao_eletromagnetica_2640