Reasoning Claim Tokens (RCTs): A Minimal Evidentiary Primitive for Governing AI-Mediated Representations

As generative AI systems increasingly mediate how enterprises are represented to consumers, regulators, and markets, organizations face a governance gap: consequential AI outputs are produced by systems they do not control and cannot inspect internally. Existing approaches emphasize accuracy, optimization, or interpretability, but none provide a minimal evidentiary layer suitable for post-incident accountability. This document defines Reasoning Claim Tokens (RCTs) as a governance primitive designed to address this gap. RCTs capture discrete, time-indexed reasoning claims expressed by AI models during inference, without intervening in or explaining internal model logic. They provide a model-agnostic, reconstructable record of expressed reasoning sufficient to support audit, legal review, and risk governance. RCTs are positioned as evidentiary artifacts, not optimization tools, and are intended to govern external AI representations where internal model access is unavailable.