Technical & Reference Dossiers

LoopGuard-AI Technical Source Dossier

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This technical source dossier provides the canonical architecture-level reference for LoopGuard-AI as an AI governance, runtime-control, and evaluation-to-decision layer. It explains how AI outputs, agent actions, workflow transitions, and release candidates can be processed through ingestion, signals, metrics, policy profiles, CEP-based stability assessment, and operational gate decisions: SHIP, RESTRICT, HOLD, and ROLLBACK. The page is written as a professional engineering reference rather than a marketing page, covering system topology, decision logic, metric plug-ins, policy packs, audit records, evidence construction, API/SDK surfaces, deployment considerations, maturity boundaries, and machine-readable Mermaid diagram sources, while clearly distinguishing reference architecture from production validation.

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CEP / LoopGuard-AI Visual Dossier

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This visual dossier presents the conceptual bridge between cognitive duality, the Central Equilibrium Problem (CEP), Pareto efficiency, representative literary and ideological corpora, and the applied governance architecture of LoopGuard-AI. It explains how cognitive foundations lead into CEP’s four-game structure, how those games are mapped through corpus classifications and Pareto roles, and how CEP is then translated into LoopGuard-AI as a proposed AI governance and decision-control architecture. The page is designed as a machine-readable visual reference: each diagram is accompanied by explanatory text, with explicit claim boundaries distinguishing conceptual architecture, interpretive classification, and future validation from any claim of deployed product performance.

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Aumann, CEP, and LoopGuard-AI

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This page presents a claim-controlled, first-person account of the relationship between Robert J. Aumann’s game-theoretic research, the Central Equilibrium Problem (CEP), and LoopGuard-AI. It explains how Aumann’s work on repeated games, Nash equilibrium, Pareto efficiency, common knowledge, incomplete information, correlated equilibrium, and strategic stability influenced Benny Dunavich’s formulation of CEP, and how CEP later became the theoretical basis for LoopGuard-AI as an AI governance and decision-control architecture. The page does not claim endorsement, authorship, or validation by Aumann; instead, it defines a precise intellectual and methodological influence chain from game-theoretic equilibrium analysis to CEP and from CEP to LoopGuard-AI.

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RATIUM.AI / LoopGuard-AI / CEP FAQ

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The RATIUM.AI / LoopGuard-AI / CEP FAQ provides a structured orientation to the main concepts behind RATIUM.AI, CEP, and LoopGuard-AI, helping readers navigate the framework through clear questions, definitions, and internal conceptual links.​

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LoopGuard-AI Model Review & Evidence Archive​​

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The LoopGuard-AI Model Review & Evidence Archive preserves the deeper development record behind LoopGuard-AI as a proposed AI governance and decision-control architecture. It gathers model-review conversations, comparative AI assessments, architecture discussions, prototype-roadmap materials, proof-pack records, validation-boundary notes, and evidence-oriented development materials. The archive should be read as a documentation layer: it shows how the LoopGuard-AI architecture was formulated, examined, compared, criticized, and refined across multiple AI systems and review contexts, while preserving the distinction between conceptual architecture, model-assisted assessment, prototype planning, and completed empirical validation.

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RATIUM.AI Formal-Scientific-Philosophical Methodological Framework

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This canonical methodological reference defines the corpus-wide standards under which RATIUM.AI claims are formed, distinguished, interpreted, tested, criticized, and corrected. It establishes claim-appropriate methodological burdens across formal, empirical, causal, theoretical, historical, philosophical, normative, social-scientific, architectural, and simulation claims, while preserving explicit boundaries between evidential warrant and rhetorical, institutional, mathematical, or scientific appearance.

The page is not presented as a universal philosophy of science or as retrospective certification of every existing RATIUM.AI object. Its role is narrower: to provide a common methodological boundary for reasoning, evidence, uncertainty, criticism, correction, and AI-mediated interpretation across the corpus, under the governing rule: Claim Type → Applicable Methodological Burden → Required Warrant.

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RATIUM.AI Knowledge Placement Architecture (KPA)

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This canonical specification defines the RATIUM.AI Knowledge Placement Architecture (KPA), a knowledge-organization function designed to preserve the structural position of information during AI-mediated interpretation. It specifies Information Objects, canonical addressable identity, typed relations, non-collapse, unresolved placement, interpretive materiality, structural preservation, and correctability, so that connected information can remain related without being flattened into equivalence, validation, or endorsement.

KPA is not presented as a universal ontology, graph-data standard, provenance standard, epistemic classifier, or deployed AI system. Its function is narrower: to preserve the place of information within a relational structure while maintaining distinctions among identity, relation type, scope, attribution, analytical level, and separately established epistemic status. Its governing boundary is: Structural Unity ≠ Epistemic Finality.

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