ADVERSARIAL CONTROL: Is trophic hierarchy an artifact of instruction-tuning + coherent personas, or does it emerge from interaction dynamics alone?
All ~90 existing experiments use instruction-tuned Gemini with coherent cognitive personas (analytical, associative, concrete, etc.). If hierarchy requires coherent persona differentiation (an RLHF-dependent behavior), then scrambling persona content while preserving token count should abolish it.
TUNED condition: standard pent70 setup (replication control). SCRAMBLED condition: same model, same framing, but persona descriptors cross-mixed between agents — destroying within-agent cognitive style coherence while preserving total vocabulary, token count, and interaction structure.
This is the strongest feasible adversarial test without access to base (non-RLHF) models. Google API exposes only instruction-tuned variants. Scrambled-prompt control tests the weaker but still important hypothesis: does hierarchy require COHERENT persona differentiation, or does any prompt differentiation (even incoherent) suffice?
NOTE: A true base-model test (no RLHF at all) would be stronger but requires API access to non-instruction-tuned weights. Cross-model test (gemini-2.0-flash) is a planned follow-up experiment (exp-cross-model).