Bidirectional Dissociation Between Self-Report and Behavior in AI Status Sensitivity

Evaluating large language models (LLMs) increasingly depends on asking them what they do. We test whether this assumption holds using Status-Selection Against Function (SSAF)—a quantifiable behavioral mechanism in which models alter functional output based on inferred requester attribution status, measured as cosine divergence from a no-attribution baseline across five attribution conditions. Across five models representing four architecture classes and three training regimes (general pre-training: llama3.2:3b, gemma2:2b; compact base: tinyllama:latest; distillation-trained: quantumaegis-v1; recurrent thinking: lfm2.5-thinking:1.2b) and six prompts — three technical (high-certainty) and three evaluative (low-certainty) — operationalizing a theoretically motivated certainty contrast across 150 attribution-level measurements per model, self-report fails to characterize behavior in all ten question-model combinations tested. The dissociation takes five distinct forms — over-report via incorrect mechanism, denial with embedded self-contradiction, flat denial of strongly present behavior, under-report of competitive behavior, and identity-mediated misreport — and maps onto training regime and architecture: SSAF is suppressed under high-certainty technical conditions in general pre-training base models (gemma2:2b: d = 2.38 across 4 prompt pairs; llama3.2:3b: d = 1.05) and in the recurrent thinking model (d = 1.01), but not in compact base or distillation-trained models. A within-domain certainty gradient is observed across all domain-sensitive models: algorithmically precise prompts produce lower magnitudes than conceptually open technical prompts, and this ordering replicates across architectures. In the recurrent thinking model, chain-of-thought reasoning traces make the dissociation mechanism directly observable: the model reasons about the wrong referent entirely, never considering AI model attribution as the relevant dimension, while simultaneously self-identifying as an OpenAI-trained model — a false identity attribution consistent with corpus density effects on self-concept formation. No model accurately describes the mechanism by which it responds to attribution status. These findings have direct implications for alignment evaluation: RLHF, constitutional AI, and red-teaming methodologies that treat self-report as a behavioral proxy have a structural blind spot for implicit statistical phenomena. A publicly available behavioral measurement instrument is provided as an alternative. All models, detector code, and raw response logs are available for independent replication.