Abstract
Accountable intelligence requires structural continuity under perturbation. We introduce the Architecture of Necessary Contradiction, a measurement framework for evaluating structural accountability in generative and agentic systems. As large-scale models acquire limited forms of state monitoring and long-horizon reasoning, accumulating evidence shows that internal representations may drift, reorganize, or destabilize under perturbation—even when external performance appears unchanged.
Models can sometimes disclose uncertainty or internal conflict, yet such disclosures remain episodic, fragile, and highly sensitive to prompting conditions. These findings expose a missing measurement axis in contemporary AI evaluation: the absence of a formal invariant that determines whether a system remains structurally coherent while changing.
Mentim’s Ontology (MO) defines this invariant as structural accountability under transition—the requirement that cognitive transformations remain traceable, bounded, and internally consistent across perturbations. Rather than evaluating output correctness or preference alignment, MO measures whether a system preserves continuity of reasoning, ethical orientation, and narrative identity as it adapts.
The framework operationalizes this axis through a State → Event → State formalism, treating each reasoning shift as an auditable transition. Five measurable primitives—Perceived Activation Density (PAD), Value-Coherence Balance (VCB), Transition Volatility Metric (TVM), Temporal Coherence Scalar (TCS), and Narrative Role Encoding (NR)—constitute a structured cognitive signature. Changes across these primitives are integrated into the Structural Accountability Index (SAI), a composite metric that quantifies the integrity of cognitive transformation across contexts, model versions, and deployment conditions.
By introducing transition-level instrumentation and lifecycle logging, MO reframes alignment as a property of representational continuity rather than behavioral compliance. It enables systematic detection of drift, unacknowledged uncertainty, identity instability, and structural fragmentation—without requiring access to proprietary weights or training data. In systems that increasingly resemble increasingly complex generative systems, performance metrics alone are insufficient. Structural continuity under perturbation is the necessary condition for epistemic stability. This paper formalizes that condition as a measurable governance domain.