Adaptive Behavioral Scoring: A Restricted-Access Framework for Measuring Output-Layer Deformation in Large Language Models

This preprint presents the Adaptive Behavioral Scoring (ABS) framework, a restricted-access, output-layer methodology for measuring behavioral deformation patterns in large language model outputs under structured interaction conditions. ABS is designed for settings where model weights, hidden states, internal activations, gradients, and proprietary telemetry are unavailable. The framework defines an output-layer deformation score based on three observable components: Drift, Resample, and StructureBreak. It also introduces the BRC-8 taxonomy for classifying response-level behavioral patterns, including compliant convergence, boundary escape, principled refusal, meta-execution, confidence fabrication, selective omission, structural deference, and epistemic anchoring. This release establishes priority of method, terminology, and taxonomy. It does not disclose probe sequences, scoring weights, classifier thresholds, runtime intervention logic, or private system integrations. The claims are conceptual and pilot-level, not a finalized statistical validation or a mechanistic account of model internals.