This paper introduces a bidirectional method for evaluating coherence in both human authored philosophical systems and contemporary AI models. The approach treats coherence as the preservation of structural invariants under transformation, and operationalizes this through a sequence of compression, expansion, boundary, counterexample, reconstruction, and cross domain mapping tests. When applied to a philosophical text, these transformations reveal points where the argument requires additional scaffolding or clarification. When applied to an AI model interpreting the same text, the same transformations expose characteristic model failure modes, including coherence drift, level confusion, over generalization, hallucinated structure, and instability in reconstructing multi step reasoning. The result is a dual system evaluation protocol in which each side—human and machine—functions as a stress test for the other. I argue that this method provides a practical framework for strengthening philosophical clarity, diagnosing conceptual drift, and identifying the limits of statistical reasoning systems. The protocol offers a general tool for philosophical methodology and a complementary benchmark for AI interpretive stability, suggesting that coherence based evaluation can serve as a bridge between human conceptual work and machine reasoning.
Denis Bailey (2026) studied this question.