Sovereign Epistemological Memory and Reasoning System: A Trilaminar Epistemological Architecture for Agentic AI

Sovereign Epistemological Memory and Reasoning System: A Trilaminar Epistemological Architecture for Agentic AI The ambition to build a 'Brain' for the knowledge worker is hampered by an epistemic crisis: information is perceived as dispersed, unstructured, and unverifiable. Standard RAG systems perpetuate this by treating knowledge as flat labeled entities — accumulating without epistemological rigor. This paper introduces Bastian A-000, an agentic AI memory and reasoning system designed to resolve this crisis architecturally. Its atomic unit of knowledge — the Symbolat — exists as a three-dimensional entity comprising intentional signal (BIO), documentary fact (PHY), and derived inference (ABS). This structural correspondence with Peirce's triadic categories establishes a computable epistemology for sovereign AI. (1) Sovereign Temporal Physics (C24 Metronome): Time is implemented as an irreversible Virtual Tick Counter rather than a metadata timestamp, enabling causal verification and knowledge decay as structural properties. (2) Deterministic Meta-Cognitive Arbitration (G_A): The Authenticity Density Indicator (G_A = PHY / (ABS + 0.01)) provides a computable measure of factual grounding, enabling the system to architecturally bypass external retrieval when internal knowledge suffices — without reinforcement learning. (3) Semantic Drift Detection (S_pryt): The Instability Coefficient tracks change in G_A across sessions, detecting when knowledge becomes unstable before performance degradation occurs. (4) Epistemological Retrospection: When a new epistemic mechanism is introduced, all prior knowledge atoms are retroactively revaluated through the new apparatus — a form of self-improvement distinct from continual learning and knowledge graph refinement. (5) Epistemic Self-Correction in Production: A documented case study demonstrates the system applying its own B-NTP Gate diagnostic to its own textual output, identifying KLASA-1 hallucination (high ABS, low PHY) and producing a corrected, PHY-grounded version without external intervention. The architecture is grounded philosophically in Peirce's triadic categories (BIO/PHY/ABS as Firstness/Secondness/Thirdness), Kępiński's information metabolism (sedimentation over registration), and Popper's falsifiability hierarchy (PHY layer typing). The paper positions Bastian as a response to the meta-cognitive deficit identified by Yan et al. (2026) and to broader challenges in building knowledge systems for knowledge workers (Fant, 2026; Pinar, 2026). Developed by a single independent researcher using Python and Gemini 2.5 Flash, Claude.