Epistemic Lattice AI: A Formal Model of Auto-Interrogative Learning for Accountable Autonomy

This deposit contains the manuscript for the paper "Epistemic Lattice AI: A Formal Model of Auto-Interrogative Learning for Accountable Autonomy." The Core Problem: Modern AI, built on statistical deep learning, has achieved remarkable competence but remains fundamentally opaque. This creates a governance crisis as autonomous systems are deployed in high-stakes domains—they can act, but cannot justify their actions in a human-legible, auditable way. The Proposed Solution: This paper introduces Epistemic Lattice AI (ELA), a novel cognitive architecture designed to bridge this gap. ELA formalizes knowledge as a dynamic, interlocking lattice of contextualized premises. Its core innovation is an Auto-Interrogation Engine that drives learning not through parameter tuning, but through the structured expansion and validation of its knowledge lattice. Key Contributions: The Interlocking Premise Cube (IPC) Model: A formal framework for representing knowledge as premises within explicit contexts. From Statistical Learning to Epistemic Growth: A theoretical shift framing learning as lattice expansion rather than optimization. Governance by Design: Demonstration of how the IPC model provides native explainability, dynamic audit trails, and structural oversight—directly addressing threats like Agentic Parapsychological Cybercrime (APψC). A Practical Research Agenda: A staged pathway for implementing ELA, addressing challenges like scalability and the frame problem. Why It Matters: The paper argues that the next phase of AI is not about greater scale, but about building systems that can operate legitimately within human legal and ethical frameworks. ELA is proposed as the foundational epistemic infrastructure necessary for accountable autonomy. Contents: This upload includes the full manuscript PDF, along with any supplementary materials.