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Lumei™ Unified Context Engineering Architecture (UCEA)—a next-generation Cognitive OS

Year: 2026
Type: article
Abstract: Presentation OverviewThis repository archives the official functional specifications, structural blueprints, and conceptual design slides for the Lumei™ Unified Context Engineering Architecture (UCEA)—a next-generation Cognitive Operating System (OS) Kernel designed to enforce engineering determinism within probabilistic machine learning frameworks. This Cognitive OS is designed to substitute probabilistic guessing with structural containment. Core Conceptual: Lumei UCEA is not merely an orchestration framework; it is designed as a Cognitive Operating System (OS) Kernel. It introduces a proposed staged, typed 6-layer transduction pipeline: Morphology: Atomic extraction of raw facts. Lexicon: Domain-ontology mapping via Knowledge SKU Packs. Syntax: Relational topology extraction. Semantics: Deep intent inference. Pragmatics (The VETO Engine): Programmatic calculation of Contradiction Density. Testable Meaning Representation (TMR): Schema-validated, JSON-structured terminal output. This architecture is governed by 3 core theoretical axes: First, the Constraint-Governed Prompting Fields (C-GPF) & foundational SACS-LO architecture (Rujirawanich, 2026; DOI: 10.5281/zenodo.20112224, DOI: 10.5281/zenodo.20251106) establishes the transition from open-ended generative instructions to Structural Boundary Conditions. By imposing predefined constraints upon the input prior to execution, C-GPF shapes the model's latent space (Latent Space Shaping), effectively restricting the probability distribution of outputs to pre-approved semantic manifolds. This ensures that the model operates within an engineered ontological boundary rather than an unconstrained probabilistic vacuum. Second, The Holy Trinity of Context Engineering (Rujirawanich, 2026; DOI: 10.5281/zenodo.20662488) provides the mathematical rigor required for Epistemic Reduction. By conceptualizing semantic ambiguity as a high-dimensional continuous space, this theory employs mathematical boundaries analogous to Voronoi Tessellations to delineate rigid operational zones within the AI's "cognitive core." Information is systematically reduced and forced into discrete, non-overlapping conceptual partitions, thereby eradicating the semantic ambiguity that typically plagues large context vectors. Third, Flow Theory in the Agentic Era (Rujirawanich, 2026; DOI: 10.5281/zenodo.20720633) operationalizes the psycho-computational dynamic between the human operator and the machine. We formalize this interaction through the equation of Phase-Locked Resonance, representing a state where the AI's cognitive velocity and the human's epistemic intent are perfectly synchronized. By maintaining this Optimal Innovation Flow State, the architecture actively mitigates the dual risks of Automation Bias (over-reliance on stochastic outputs) and Human Overload (cognitive fatigue from excessive micro-management). Related WorksThis slide deck serves as Technical Note 03 (TN03) within the author's overarching research protocol. It acts as the upstream theoretical target architecture for the practical software implementation validated in the BoardroomVoiceAgent Repository (10.5281/zenodo.21003118 — SACS-LO_TN02_BoardroomVoiceAgent). Conceptual priority, mathematical formulations, and latent orchestration mechanics are established under the foundational Lumei™ SACS-LO architecture. This slide deck contains functional specifications, structural blueprints, and conceptual design supplement to the foundational SACS-LO architecture Technical Note (TN01) registered under DOI: 10.5281/zenodo.20251106 (SACS-LO TN01) and Constraint-Governed Prompting Fields (DOI: 10.5281/zenodo.20112224). ---Licensing: Creative Commons Attribution 4.0 International (CC BY 4.0)Declaration of Generative AI and Accountability This manuscript was developed through a structured collaboration between the author and a generative AI system based on Large Language Models, utilizing the author's proprietary SACS-LO architecture. The integration of AI was a deliberate methodological choice intended to challenge authorial bias and explore new structural syntheses. Human Authorship and Oversight: The author maintains full creative control and sovereign accountability for the content of this work. The AI was employed as a sophisticated drafting tool, while the final expression, structural organization, and substantive synthesis were performed and decided by the author. Every section has been critically revised, restructured, and rewritten to ensure intellectual integrity, fulfilling the requirements of human authorship. Accountability Standard: In alignment with the spirit of the NIST AI Risk Management Framework, the author implemented a rigorous human-in-the-loop (HITL) process to govern the output, mapping potential AI-generated errors and iteratively validating the results. The author assumes sole legal and ethical responsibility for the entirety of this manuscript.
DOI: 10.5281/zenodo.21004727
Language: English
Open Access status: green
OpenAlex ID: W7166441435
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