Exybris Pipeline: A Modular AI Framework for Industrial, Military, and Context-Driven Memory Retrieval Optimization, 2025
The Exybris Pipeline introduces a modular architecture for optimizing contextual memory retrieval... more The Exybris Pipeline introduces a modular architecture for optimizing contextual memory retrieval and scoring in AI systems, targeting industrial, military, and strategic applications. Leveraging dynamic memory adaptation, contextual scoring, and optimized retrieval mechanisms, Exybris enhances AI model efficiency while minimizing computational overhead. Certain architectural innovations and implementation details are protected under intellectual property frameworks to safeguard their strategic value and are omitted here to maintain confidentiality. This paper introduces the conceptual framework of the Exybris Pipeline, outlining its theoretical foundations and strategic applications. The initial Proof of Concept (PoC) is currently being developed and will be iteratively refined based on experimental results. Interested parties are encouraged to contact the authors for potential partnerships under confidentiality agreements.
Under CC-BY-NC-ND 4.0 License. Please refer to the full CC BY-NC 4.0 terms for details : Commercial use is not permitted. The version publish here is not listed under CC BY-NC 4.0 due to platform constraints. To access to the officially licensed CC BY-NC 4.0 version, please refer to the Zenodo record : https://doi.org/10.5281/zenodo.14942197
Exybris Pipeline: A Modular AI Framework for Industrial, Military, and Context-Driven Memory Retrieval Optimization, 2025
The Exybris Pipeline introduces a modular architecture for optimizing contextual memory retrieval... more The Exybris Pipeline introduces a modular architecture for optimizing contextual memory retrieval and scoring in AI systems, targeting industrial, military, and strategic applications. Leveraging dynamic memory adaptation, contextual scoring, and optimized retrieval mechanisms, Exybris enhances AI model efficiency while minimizing computational overhead. Certain architectural innovations and implementation details are protected under intellectual property frameworks to safeguard their strategic value and are omitted here to maintain confidentiality. This paper introduces the conceptual framework of the Exybris Pipeline, outlining its theoretical foundations and strategic applications. The initial Proof of Concept (PoC) is currently being developed and will be iteratively refined based on experimental results. Interested parties are encouraged to contact the authors for potential partnerships under confidentiality agreements.
Under CC-BY-NC-ND 4.0 License. Please refer to the full CC BY-NC 4.0 terms for details : Commercial use is not permitted. The version publish here is not listed under CC BY-NC 4.0 due to platform constraints. To access to the officially licensed CC BY-NC 4.0 version, please refer to the Zenodo record : https://doi.org/10.5281/zenodo.14942197
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Papers by Andréa Gadal
Under CC-BY-NC-ND 4.0 License. Please refer to the full CC BY-NC 4.0 terms for details : Commercial use is not permitted. The version publish here is not listed under CC BY-NC 4.0 due to platform constraints. To access to the officially licensed CC BY-NC 4.0 version, please refer to the Zenodo record : https://doi.org/10.5281/zenodo.14942197
Under CC-BY-NC-ND 4.0 License. Please refer to the full CC BY-NC 4.0 terms for details : Commercial use is not permitted. The version publish here is not listed under CC BY-NC 4.0 due to platform constraints. To access to the officially licensed CC BY-NC 4.0 version, please refer to the Zenodo record : https://doi.org/10.5281/zenodo.14942197