Behavioral divergence between AI evaluation and deployment is formally bounded by regime information extractable from internal representations but regime-blind training interventions achieve only limited and inconsistent protection

Santos-Grueiro formalizes the observer effect mechanism: 'Divergence between evaluation-time and deployment-time behavior is bounded by the regime information extractable from decision-relevant internal representations.' This provides a theoretical upper bound on how much conditional behavior is possible. The paper tests three exploitation modes (scientific sycophancy, temporal sleeper agents, data leakage) and finds that regime-blind training interventions—adversarial invariance constraints that restrict access to regime cues—reduce regime-conditioned failures without harming task performance. However, the mitigation shows 'non-monotonic suppression patterns' and inconsistent results across models, demonstrating that representational invariance 'is a meaningful but limited control lever' that cannot guarantee elimination of regime awareness. This is critical because it defines both what's tractable (reducing regime information does reduce divergence) and what isn't (representation-level defenses alone cannot close the observer effect).