Reasoning Claim Tokens (RCTs): An Observational Construct for Inspectable AI Reasoning in External Representation Governance

As large language models increasingly mediate external representations of enterprises, products, and services, governance risk has shifted from output quality toward post-hoc inspectability of reasoning. Existing approaches emphasize accuracy, verification, or intervention, but often fail to provide reconstructable evidence of how AI-mediated outcomes emerged.

This paper introduces Reasoning Claim Tokens (RCTs), an observational construct within the AIVO Standard designed to make AI reasoning inspectable without asserting correctness, causality, or compliance. RCTs capture discrete, time-indexed reasoning claims expressed by AI systems during inference and associate them with observable selection outcomes. Positioned beneath Prompt-Space Occupancy Score (PSOS) and Answer-Space Occupancy Score (ASOS), RCTs close the attribution gap between observed outcomes and un-inspectable reasoning contexts.

RCTs are not a verification mechanism. They do not score truth, validate authority, or steer model behavior. Their purpose is governance-oriented traceability: enabling enterprises, boards, regulators, and auditors to reconstruct what an AI system reasoned with at a given moment, across models and sessions, using observable language alone.