Adversarial Validation Protocol v1.4 for Behavioral Claims in Artificial Intelligence Systems

This paper presents the Adversarial Validation Protocol v1.4 for evaluating behavioral claims in artificial intelligence systems. The protocol operates as an adversarial stress-test for behavioral assertions, rather than a proof of consciousness. It introduces a structured verification workflow featuring preregistered hypotheses, mandatory countermodels, systematic adversarial attacks, and five independent validation gates. The primary contribution of this work is a scalable framework for ruling out false positives in complex AI behavior through empirical falsification. The protocol does not establish phenomenal consciousness; instead, it determines whether a given behavioral claim can survive rigorous, non-conscious alternative explanations. LIMITATIONS, FAILURE MODES, AND ALTERNATIVE HYPOTHESES (H0): The protocol is explicitly limited to falsifying specified behavioral hypotheses. It possesses no mechanism to determine subjective, phenomenal experience. Five primary systemic failure modes are identified and mitigated within the architecture: 1. Adversarial Mimicry: A non-conscious model trained on the evaluation benchmark may artificially reproduce the target behavioral signature. Mitigation: The protocol enforces a pre-specified countermodel discrimination margin (e.g., >= 0.05 AUROC). If the system under test matches the countermodel within this margin, the consciousness-relevant hypothesis fails. 2. Prompt Conditioning: The model may generate target behaviors exclusively within highly brittle prompt structures. Mitigation: The protocol mandates systematic ablation of prompt wording, semantic context, and token formatting to establish behavioral invariance. 3. Reward Optimization: Target behaviors may be artifacts of hidden reward signals or reinforcement histories. Mitigation: Core tests require strict isolation from active reward models and historical RLHF dependencies. 4. Dataset Contamination: Pre-training data may contain direct instances or logical approximations of the evaluation benchmarks. Mitigation: The protocol mandates clean-room training environments or strict token-overlap audits for primary verification data. 5. Evaluator Bias: Human or automated evaluators may project intent, introducing systemic confirmation bias. Mitigation: Mandatory double-blinding and de-identified telemetry datasets across all evaluation phases. An adversarial agent attempting to spoof this evidence framework will be systematically intercepted by the countermodel gate, the structural ablation requirements, or the blinded telemetry validation.