Synthetic Epistemology: Engineering Coherence Beyond Truth

The rise of large language models, autonomous inference engines, and symbolic simulators has outpaced the capacity of traditional epistemology to evaluate, validate, or constrain their outputs. Classical truth-centered frameworks, built on correspondence, justification, and reference, fail when applied to agents that generate plausible, recursive, internally consistent knowledge without external grounding. Synthetic Epistemology is a coherence-first framework designed to assess and guide knowledge systems where truth cannot yet be verified. Rather than asking whether an output is factually accurate, it asks whether it preserves, amplifies, and recursively sustains coherent structure across symbolic transformations. This discipline introduces tools and metrics such as coherence delta (ΔC), commutator residue (Ξ), and recursive audit protocols to evaluate whether a symbolic output can support inference, regeneration, and structural adaptation, regardless of truth value. Synthetic Epistemology does not replace empirical science or classical reasoning. It scaffolds cognition in contexts of uncertainty, hallucination, fiction, or simulation. It is essential wherever agents must operate with incomplete, fabricated, or pluralistic data, be it in artificial intelligence, post-truth governance, or speculative model design. This white paper defines Synthetic Epistemology as a formal discipline, outlines its foundational principles, and demonstrates its strategic relevance to AI safety, epistemic pluralism, and coherence-based evaluation in synthetic systems.