CAMAF v2.0.1: A Metric Framework for Epistemological Robustness Audit in AI-Mediated Environments

This technical report introduces the Critical Analytical Metadata & Assessment Framework (CAMAF) v2.0.1, a formal protocol designed to audit informational integrity and mitigate hallucinations in outputs generated by Large Language Models (LLMs). Current approaches to model evaluation often rely on binary fact-checking or benchmark accuracy. CAMAF proposes an alternative methodology based on epistemological decomposition, enabling the structural analysis of claims within generated narratives and the measurement of the distance between empirical evidence and inferred conclusions. The framework introduces a quantitative architecture composed of the following elements: Key Components EIU (Epistemological Information Unit)The fundamental metric unit used to decompose generated outputs into atomic epistemic claims. ERS (Epistemological Robustness Score)A metric designed to quantify the structural reliability of a claim based on evidentiary density and validation across sources. EDR (Epistemological Deviation Risk)A probabilistic indicator that measures the risk of narrative drift, inferential inflation, and informational entropy. δ Operator (Domain Calibration Operator)A domain-specific calibration mechanism used to adjust evidentiary weighting across heterogeneous knowledge environments. By operationalizing principles from classical epistemology, including concepts derived from Karl Popper, Edwin Thompson Jaynes, and Thomas Kuhn, CAMAF provides a machine-readable audit layer capable of supporting scalable trust engineering for artificial intelligence systems.CAMAF is intended as a meta-analytical framework for evaluating informational reliability in AI-generated content across scientific, policy, and public discourse domains.